healthcare

Neural networks have emerged as a transformative force across various sectors, revolutionizing industries such as healthcare, finance, and automotive technology.

Inspired by the human brain, artificial neural networks (ANNs) leverage bio-inspired computational models to solve complex problems and perform tasks previously exclusive to human intelligence.

The effectiveness of neural networks largely hinges on the quality and quantity of data used for training, underlining the significance of robust datasets in achieving high-performing models.

Ongoing research and development signal a future where neural network applications could expand even further, potentially uncovering new ways to address global challenges and drive progress in the digital age.

In this blog, we will explore the current applications of neural networks across 7 different industries, exploring the different enhanced aspects of each.

Customer Support

Chat Systems

They have transformed customer support through chat systems. By analyzing customer queries and past conversations, neural networks understand the context and provide relevant and accurate responses.

This technology, known as natural language processing (NLP), enables chatbots to interact with customers in a conversational manner, providing instant and personalized support.

Continuous Improvement

These systems learn from extensive datasets of customer interactions, empowering businesses to address inquiries efficiently, from basic FAQs to complex troubleshooting. Companies like Talla use neural networks to enhance their AI-driven customer support solutions.

Proactive Support

Neural networks anticipate potential issues based on historical interactions, improving the overall customer experience and reducing churn rates. This proactive approach ensures that businesses can address problems before they escalate, maintaining customer satisfaction.

Read more to explore the Basics of Neural Networks

Telecommunication

Network Operations

Neural networks boost telecommunications performance, reliability, and service offerings by managing and controlling network operations. They also power intelligent customer service solutions like voice recognition systems.

Data Compression and Optimization

Neural networks optimize network functionality by compressing data for efficient transmission and acting as equalizers for clear signals. This improves communication experiences for users and ensures optimal network performance even under heavy load conditions.

Enhanced Communication

These network architectures enable real-time translation of spoken languages. For example, Google Translate uses neural networks to provide accurate translations instantly.

This technology excels at pattern recognition tasks like facial recognition, speech recognition, and handwriting recognition, making communication seamless across different languages and regions.

Learn more about the 5 Main Types of Neural Networks

Healthcare

Medical Diagnosis

These networks drive advancements in medical diagnosis and treatment, such as skin cancer detection. Advanced algorithms can distinguish tissue growth patterns, enabling early and accurate detection of skin cancer.

For instance, SkinVision, an app that uses neural networks for skin cancer detection, has a specificity of 80% and a sensitivity of 94%, which is higher than most dermatologists.

Personalized Medicine

They analyze genetic information and patient data to forecast treatment responses, enhancing treatment effectiveness and minimizing adverse effects. IBM Watson is an example that uses neural networks to analyze cancer patient data and suggest personalized treatment plans, tailoring interventions to individual patient needs.

Medical Imaging

Neural networks analyze data from MRI and CT scans to identify abnormalities like tumors with high precision, speeding up diagnosis and treatment planning. This capability reduces the time required for medical evaluations and increases the accuracy of diagnoses.

Drug Discovery

Neural networks predict interactions between chemical compounds and biological targets, reducing the time and costs associated with bringing new drugs to market. This accelerates the development of new treatments and ensures that they are both effective and safe.

Finance

Stock Market Prediction

These deep-learning architectures analyze historical stock data to forecast market trends, aiding investors in making informed decisions. Hedge funds use neural networks to predict stock performance and optimize investment strategies.

Fraud Detection

They scrutinize transaction data in real-time to flag suspicious activities, safeguarding financial institutions from potential losses. Companies like MasterCard and PayPal use neural networks to detect and prevent fraudulent transactions.

Risk Assessment

Neural networks evaluate factors such as credit history and income levels to predict the likelihood of default, helping lenders make sound loan approval decisions. This capability ensures that financial institutions can manage risk effectively while providing services to eligible customers.

Automotive

Autonomous Vehicles

The automotive industry harnesses these networks in autonomous vehicles and self-driving cars. These networks interpret sensor data to make real-time driving decisions, ensuring safe and efficient navigation. Tesla and Waymo are examples of companies using neural networks in autonomous driving technologies.

Traffic Management

Neural networks help manage traffic and prevent accidents by predicting congestion, optimizing signal timings, and providing real-time hazard information. This leads to smoother traffic flow and reduces the likelihood of traffic-related incidents.

Vehicle Maintenance

Neural networks predict mechanical failures before they occur, facilitating timely repairs and prolonging vehicle lifespan. This proactive approach helps manufacturers like BMW maintain vehicle reliability and performance.

Aerospace

Fault Detection

These networks detect faults in aircraft components before they become problems, minimizing the risk of in-flight failures. This enhances the safety and reliability of air travel by ensuring that potential issues are addressed promptly.

Autopilot Systems

They also enhance autopilot systems by constantly learning and adapting, contributing to smoother and more efficient autopiloting. This reduces the workload on human pilots and improves flight stability and safety.

Flight Path Optimization

Neural networks simulate various flight paths, allowing engineers to test and optimize routes for maximum safety and fuel efficiency. This capability helps in planning efficient flight operations and reducing operational costs.

Learn more about Deep Learning Using Python in the Cloud

Manufacturing

Process Optimization

These networks design new chemicals, optimize production processes, and predict the quality of finished products. This leads to better product design and fewer defects. Companies like General Electric use neural networks to enhance their manufacturing processes.

Predictive Maintenance

They can also identify potential equipment problems before they cause costly downtime, allowing for proactive maintenance and saving time and money. This application is used by companies like Unilever to maintain operational efficiency.

Quality Inspection

They monitor production in real-time, ensuring consistent quality. They can even inspect visual aspects like welds, freeing up human workers for more complex tasks. This technology is widely used in the automotive and electronics industries.

What are the Future Applications of Neural Networks?

Integration with AI and Robotics

Combining neural networks with AI and robotics creates advanced autonomous systems capable of performing intricate tasks with human-like intelligence. This integration enhances productivity by allowing robots to adapt to new situations and learn from their environment.

Such systems can perform complex operations in manufacturing, healthcare, and defense, significantly improving efficiency and accuracy.

Virtual Reality

Integration with virtual reality (VR) technologies fosters more immersive and interactive experiences in fields such as entertainment and education. By leveraging neural networks, VR systems can create realistic simulations and responsive environments, providing users with a deeper sense of presence.

This technology is also being used in professional training programs to simulate real-world scenarios, enhancing learning outcomes.

Environmental Monitoring

These networks analyze data from sensors and satellites to predict natural disasters, monitor deforestation, and track climate change patterns. These systems aid in mitigating environmental impacts and preserving ecosystems by providing accurate and timely information to decision-makers.

As neural networks continue to expand into new domains, they offer innovative solutions to pressing challenges, shaping the future and creating new opportunities for growth.

What are vector embedding tools?

Vector embeddings are a specific embedding tool that uses vectors for data representation. While the direction of a vector determines its relationship with other data points in space, the length of a vector signifies the importance of the data point it represents.

A vector embedding tool processes input data by analyzing it and identifying key features of interest. The tool then assigns a unique vector to any data point based on its features. These are a powerful tool for the representation of complex datasets, allowing more efficient and faster data processing.

General embedding tools process a wide variety of data, capturing general features without focusing on specialized fields of interest. On the contrary, there are specialized embedding tools that enable focused and targeted data handling within a specific field of interest.

Specialized embedding tools are particularly useful in fields like finance and healthcare where unique datasets form the basis of information. Google has shared two specialized vector embedding tools, dealing with the demands of healthcare data processing.

Why does healthcare need specialized embedding tools?

Embeddings are an important tool that enables ML engineers to develop apps that can handle multimodal data efficiently. These AI-powered applications using vector embeddings encompass various industries. While they deal with a diverse range of uses, some use cases require differentiated data-processing systems.

Healthcare is one such type of industry where specialized embedding tools can be useful for the efficient processing of data. Let’s explore major reasons for such differentiated use of embedding tools.

Explore the role of vector embeddings in generative AI

Domain-specific features

Medical data, ranging from patient history to imaging results, are crucial for diagnosis. These data sources, particularly from the field of dermatology and pathology, provide important information to medical personnel.

The slight variation of information in these sources requires specialized knowledge for the identification of relevant information patterns and changes. While regular embedding tools might fail at identifying the variations between normal and abnormal information, specialized tools can be created with proper training and contextual knowledge.

Data scarcity

While data is abundant in different fields and industries, healthcare information is often scarce. Hence, specialized embedding tools are needed to train on the small datasets with focused learning of relevant features, leading to enhanced performance in the field.

Focused and efficient data processing

The AI model must be trained to interpret particular features of interest from a typical medical image. This demands specialized tools that can focus on relevant aspects of a particular disease, assisting doctors in making accurate diagnoses for their patients.

In essence, specialized embedding tools bridge the gap between the vast amount of information within medical images and the need for accurate, interpretable diagnoses specific to each field in healthcare.

A look into Google’s embedding tools for healthcare research

The health-specific embedding tools by Google are focused on enhancing medical image analysis, particularly within the field of dermatology and pathology. This is a step towards addressing the challenge of developing ML models for medical imaging.

The two embedding tools – Derm Foundation and Path Foundation – are available for research use to explore their impact on the field of medicine and study their role in improving medical image analysis. Let’s take a look at their specific uses in the medical world.

Derm Foundation: A step towards redefining dermatology

It is a specialized embedding tool designed by Google, particularly for the field of dermatology within the world of medicine. It specifically focuses on generating embeddings from skin images, capturing the critical skin features that are relevant to diagnosing a skin condition.

The pre-training process of this specialized embedding tool consists of learning from a library of labeled skin images with detailed descriptions, such as diagnoses and clinical notes. The tool learns to identify relevant features for skin condition classification from the provided information, using it on future data to highlight similar features.

Some common features of interest for derm foundation when analyzing a typical skin image include:

• Skin color variation: to identify any abnormal pigmentation or discoloration of the skin
• Textural analysis: to identify and differentiate between smooth, rough, or scaly textures, indicative of different skin conditions
• Pattern recognition: to highlight any moles, rashes, or lesions that can connect to potential abnormalities

Potential use cases of the Derm Foundation

Based on the pre-training dataset and focus on analyzing skin-specific features, Derm Foundation embeddings have the potential to redefine the data-processing and diagnosing practices for dermatology. Researchers can use this tool to develop efficient ML models. Some leading potential use cases for these models include:

Early detection of skin cancer

Efficient identification of skin patterns and textures from images can enable dermatologists to timely detect skin cancer in patients. Early detection can lead to better treatments and outcomes overall.

Improved classification of skin diseases

Each skin condition, such as dermatitis, eczema, and psoriasis, shows up differently on a medical image. A specialized embedding tool empowers the models to efficiently detect and differentiate between different skin conditions, leading to accurate diagnoses and treatment plans.

Hence, the Derm Foundation offers enhanced accuracy in dermatological diagnoses, faster deployment of models due to the use of pre-trained embeddings, and focused analysis by dealing with relevant features. It is a step towards a more accurate and efficient diagnosis of skin conditions, ultimately improving patient care.

Here’s your guide to choosing the right vector embedding model for your generative AI use case

Path Foundation: Revamping the world of pathology in medical sciences

While the Derm Foundation was specialized to study and analyze skin images, the Path Foundation embedding is designed to focus on images from pathology.

It analyzes the visual data of tissue samples, focusing on critical features that can include:

• Cellular structures: focusing on cell size, shape, or arrangement to identify any possible diseases
• Tumor classification: differentiating between different types of tumors or assessing their aggressiveness

The pre-training process of the Path Foundation embedding comprises of labeled pathology images along with detailed descriptions and diagnoses relevant to them.

Potential use cases of the Path Foundation

Using the training dataset empowers the specialized embedding tool for efficient diagnoses in pathology. Some potential use cases within the field for this embedding tool include:

Improved cancer diagnosis

Improved analysis of pathology images can lead to timely detection of cancerous tissues. It will lead to earlier diagnoses and better patient outcomes.

Better pathology workflows

Analysis of pathology images is a time-consuming process that can be expedited with the use of an embedding tool. It will allow doctors to spend more time on complex cases while maintaining an improved workflow for their pathology diagnoses.

Thus, Path Foundation promises the development of pathology processes, supporting medical personnel in improved diagnoses and other medical processes.

Transforming healthcare with vector embedding tools

The use of embedding tools like Derm Foundation and Path Foundation has the potential to redefine data handling for medical processes. Specialized focus on relevant features offers enhanced diagnostic accuracy with efficient processes and workflows.

Moreover, the development of specialized ML models will address data scarcity often faced within healthcare when developing such solutions. It will also promote faster development of useful models and AI-powered solutions.

While the solutions will empower doctors to make faster and more accurate diagnoses, they will also personalize medicine for patients. Hence, embedding tools have the potential to significantly improve healthcare processes and treatments in the days to come.

Traditional databases in healthcare struggle to grasp the complex relationships between patients and their clinical histories. This limitation hinders personalized medicine and hampers rapid diagnosis. Vector databases, with their ability to store and query high-dimensional patient data, emerge as a revolutionary solution.

This blog delves into the technical details of how AI in healthcare empowers patient similarity searches and paves the path for precision medicine.

Impact of AI on Healthcare

The healthcare landscape is brimming with data such as demographics, medical records, lab results, imaging scans, – the list goes on. While these large datasets hold immense potential for personalized medicine and groundbreaking discoveries, traditional relational databases cannot store such high-dimensional data at a large scale and often fall short.

Their rigid structure struggles to represent the intricate connections and nuances inherent in patient data.

Vector databases are revolutionizing healthcare data management. Unlike traditional, table-like structures, they excel at handling the intricate, multi-dimensional nature of patient information.

Each patient becomes a unique point in a high-dimensional space, defined by their genetic markers, lab values, and medical history. This dense representation unlocks powerful capabilities discussed later.

Working with vector data is tough because regular databases, which usually handle one piece of information at a time, can’t handle the complexity and large amount of this type of data. This makes it hard to find important information and analyze it quickly.

That’s where vector databases come in handy—they are made on purpose to handle this special kind of data. They give you the speed, ability to grow, and flexibility you need to get the most out of your data.

Patient Similarity Search with Vector Databases in Healthcare

The magic lies in the ability to perform a similarity search. By calculating the distance between patient vectors, we can identify individuals with similar clinical profiles. This opens a large span of possibilities.

Personalized Treatment Plans

By uncovering patients with comparable profiles and treatment outcomes, doctors can tailor interventions with greater confidence and optimize individual care. It also serves as handy for medical researchers to look for efficient cures or preventions for a disease diagnosed over multiple patients by analyzing their data, particularly for a certain period. 

Here’s how vector databases transform treatment plans:

• Precise Targeting: By comparing a patient’s vector to those of others who have responded well to specific treatments, doctors can identify the most promising options with laser-like accuracy. This reduces the guesswork and minimizes the risk of ineffective therapies.
• Predictive Insights: Vector databases enable researchers to analyze the trajectories of similar patients, predicting their potential responses to different treatments. This foresight empowers doctors to tailor interventions, preventing complications and optimizing outcomes proactively.
• Unlocking Untapped Potential: By uncovering hidden connections between seemingly disparate data points, vector databases can reveal new therapeutic targets and treatment possibilities. This opens doors for personalized medicine breakthroughs that were previously unimaginable.
• Dynamic Adaptation: As a patient’s health evolves, their vector map shifts and readjusts accordingly. This allows for real-time monitoring and continuous refinement of treatment plans, ensuring the best possible care at every stage of the journey.

Drug Discovery and Repurposing

Identifying patients similar to those successfully treated with a specific drug can accelerate clinical trials and uncover unexpected connections for existing medications.

• Accelerated exploration: They transform complex drug and disease data into dense vectors, allowing for rapid similarity searches and the identification of promising drug candidates. Imagine sifting through millions of molecules at a single glance, pinpointing those with similar properties to known effective drugs.
• Repurposing potential: Vector databases can unearth hidden connections between existing drugs and potential new applications. By comparing drug vectors to disease vectors, they can reveal unexpected repurposing opportunities, offering a faster and cheaper path to new treatments. 
• Personalization insights: By weaving genetic and patient data into the drug discovery tapestry, vector databases can inform the development of personalized medications tailored to individual needs and responses. This opens the door to a future where treatments are as unique as the patients themselves. 
• Predictive power: Analyzing the molecular dance within the vector space can unveil potential side effects and predict drug efficacy before entering clinical trials. This helps navigate the treacherous waters of development, saving time and resources while prioritizing promising candidates. 

Cohort Analysis in Research

Grouping patients with similar characteristics facilitates targeted research efforts, leading to faster breakthroughs in disease understanding and treatment development.

• Exploring Disease Mechanisms: Vector databases facilitate the identification of patient clusters that share similar disease progression patterns. This can shed light on underlying disease mechanisms and guide the development of novel diagnostic markers and therapeutic target 
• Unveiling Hidden Patterns: Vector databases excel at similarity search, enabling researchers to pinpoint patients with similar clinical trajectories, even if they don’t share the same diagnosis or traditional risk factors. This reveals hidden patterns that might have been overlooked in traditional data analysis methods.

Technicalities of Vector Databases

Using a vector database enables the incorporation of advanced functionalities into our artificial intelligence, such as semantic information retrieval and long-term memory. The diagram provided below enhances our comprehension of the significance of vector databases in such applications.

Let’s break down the illustrated process:

• Initially, we employ the embedding model to generate vector embeddings for the content intended for indexing.
• The resulting vector embedding is then placed into the vector database, referencing the original content from which the embedding was derived. 
• Upon receiving a query from the application, we utilize the same embedding model to create embeddings for the query. These query embeddings are subsequently used to search the database for similar vector embeddings. As previously noted, these analogous embeddings are linked to the initial content from which they were created.

In comparison to the working of a traditional database, where data is stored as common data types like string, integer, date, etc. Users query the data by comparison with each row; the result of this query is the rows where the condition of the query is withheld.

In vector databases, this process of querying is more optimized and efficient with the use of a similarity metric for searching the most similar vector to our query. The search involves a combination of various algorithms, like approximate nearest neighbor optimization, which uses hashing, quantization, and graph-based detection.

Here are a few key components of the discussed process described below:

• Feature engineering: Transforming raw clinical data into meaningful numerical representations suitable for vector space. This may involve techniques like natural language processing for medical records or dimensionality reduction for complex biomolecular data. 
• Distance metrics: Choosing the appropriate distance metric to calculate the similarity between patient vectors. Popular options include Euclidean distance, cosine similarity, and Manhattan distance, each capturing different aspects of the data relationships.

• • Cosine Similarity: Calculates the cosine of the angle between two vectors in a vector space. It varies from -1 to 1, with 1 indicating identical vectors, 0 denoting orthogonal vectors, and -1 representing diametrically opposed vectors.

• • Euclidean Distance: Measures the straight-line distance between two vectors in a vector space. It ranges from 0 to infinity, where 0 signifies identical vectors and larger values indicate increasing dissimilarity between vectors.

• • Dot Product: Evaluate the product of the magnitudes of two vectors and the cosine of the angle between them. Its range is from -∞ to ∞, with a positive value indicating vectors pointing in the same direction, 0 representing orthogonal vectors, and a negative value signifying vectors pointing in opposite directions. 

• Nearest neighbor search algorithms: Efficiently retrieving the closest patient vectors to a given query. Techniques like k-nearest neighbors (kNN) and Annoy trees excel in this area, enabling rapid identification of similar patients.

A general pipeline from storing vectors to querying them is shown in the figure below:

• Indexing: The vector database utilizes algorithms like PQ, LSH, or HNSW (detailed below) to index vectors. This process involves mapping vectors to a data structure that enhances search speed. 
• Querying: The vector database examines the indexed query vector against the dataset’s indexed vectors, identifying the nearest neighbors based on a similarity metric employed by that specific index. 
• Post Processing: In certain instances, the vector database retrieves the ultimate nearest neighbors from the dataset and undergoes post-processing to deliver the final results. This step may involve re-evaluating the nearest neighbors using an alternative similarity measure.

Challenges and Considerations

While vector databases offer immense potential, challenges remain:

Data Privacy and Security

Safeguarding patient data while harnessing its potential for enhanced healthcare outcomes requires the implementation of robust security protocols and careful consideration of ethical standards.

This involves establishing comprehensive measures to protect sensitive information, ensuring secure storage, and implementing stringent access controls.

Additionally, ethical considerations play a pivotal role, emphasizing the importance of transparent data handling practices, informed consent procedures, and adherence to privacy regulations. As healthcare organizations leverage the power of data to advance patient care, a meticulous approach to security and ethics becomes paramount to fostering trust and upholding the integrity of the healthcare ecosystem. 

Explainability and Interoperability

Gaining insight into the reasons behind patient similarity is essential for informed clinical decision-making. It is crucial to develop transparent models that not only analyze the “why” behind these similarities but also offer insights into the importance of features within the vector space.

This transparency ensures a comprehensive understanding of the factors influencing patient similarities, contributing to more effective and reasoned clinical decisions. Integration with existing infrastructure: Seamless integration with legacy healthcare systems is essential for the practical adoption of vector database technology.

AI in Healthcare – Opening Avenues for Precision Medicine

In summary, the integration of vector databases in healthcare is revolutionizing patient care and diagnostics. Overcoming the limitations of traditional systems, these databases enable efficient handling of complex patient data, leading to precise treatment plans, accelerated drug discovery, and enhanced research capabilities.

While the technical aspects showcase the sophistication of these systems, challenges such as data privacy and seamless integration with existing infrastructure need attention. Despite these hurdles, the potential benefits promise a significant impact on personalized medicine and improved healthcare outcomes.

A recent report by McKinsey & Company suggests that generative AI in healthcare has the potential to generate up to $1 trillion in value for the healthcare industry by 2030. This represents a significant opportunity for the healthcare sector, which is constantly seeking new ways to improve patient outcomes, reduce costs, and enhance efficiency.

However, the integration of generative AI brings both promise and peril. While its potential to revolutionize diagnostics and treatment is undeniable, the risks associated with its implementation cannot be ignored.

Read more about: How AI in healthcare has improved patient care

Let’s delve into the key concerns surrounding the use of generative AI in healthcare and explore pragmatic solutions to mitigate these risks. 

Unmasking the risks: A closer look 

1. Biased outputs:

Generative AI’s prowess is rooted in extensive datasets, but therein lies a potential pitfall – biases. If not meticulously addressed, these biases may infiltrate AI outputs, perpetuating disparities in healthcare, such as racial or gender-based variations in diagnoses and treatments. 

2. False results: 

Despite how sophisticated generative AI is, it is fallible. Inaccuracies and false results may emerge, especially when AI-generated guidance is relied upon without rigorous validation or human oversight, leading to misguided diagnoses, treatments, and medical decisions. 

3. Patient privacy:

The crux of generative AI involves processing copious amounts of sensitive patient data. Without robust protection, the specter of data breaches and unauthorized access looms large, jeopardizing patient privacy and confidentiality. 

4. Overreliance on AI: 

Striking a delicate balance between AI assistance and human expertise is crucial. Overreliance on AI-generated guidance may compromise critical thinking and decision-making, underscoring the need for a harmonious integration of technology and human insight in healthcare delivery. 

5. Ethical considerations

The ethical landscape traversed by generative AI raises pivotal questions. Responsible use, algorithmic transparency, and accountability for AI-generated outcomes demand ethical frameworks and guidelines for conscientious implementation. 

6. Regulatory and legal challenges:

The regulatory landscape for generative AI in healthcare is intricate. Navigating data protection regulations, liability concerns for AI-generated errors, and ensuring transparency in algorithms pose significant legal challenges. 

Read more about: 10 AI startups transforming healthcare

Simple strategies for mitigating the risks of AI in healthcare  

We’ve already talked about the potential pitfalls of AI in healthcare. Hence, there lies a critical need to address these risks and ensure AI’s responsible implementation. This demands a collaborative effort from healthcare organizations, regulatory bodies, and AI developers to mitigate biases, safeguard patient privacy, and uphold ethical principles.  

1. Mitigating biases and ensuring unbiased outcomes  

One of the primary concerns surrounding AI in healthcare is the potential for biased outputs. Generative AI models, if trained on biased datasets, can perpetuate and amplify existing disparities in healthcare, leading to discriminatory outcomes. To address this challenge, healthcare organizations must adopt a multi-pronged approach: 

2. Diversity in data sources:

Diversify the datasets used to train AI models to ensure they represent the broader patient population, encompassing diverse demographics, ethnicities, and socioeconomic backgrounds. 

3. Continuous monitoring and bias detection:

Continuously monitor AI models for potential biases, employing techniques such as fairness testing and bias detection algorithms. 

Human Oversight and Intervention: Implement robust human oversight mechanisms to review AI-generated outputs, ensuring they align with clinical expertise and ethical considerations. 

Safeguarding patient privacy and data security 

The use of AI in healthcare involves the processing of vast amounts of sensitive patient data, including medical records, genetic information, and personal identifiers. Protecting this data from unauthorized access, breaches, and misuse is paramount. Healthcare organizations must prioritize data security by implementing:

Learn about: Top 6 cybersecurity trends

Secure data storage and access controls:

Employ robust data encryption, multi-factor authentication, and access controls to restrict unauthorized access to patient data. 

Data minimization and privacy by design:

Collect and utilize only the minimum necessary data for AI purposes. Embed privacy considerations into the design of AI systems, employing techniques like anonymization and pseudonymization. 

Transparent data handling practices:

Clearly communicate to patients how their data will be used, stored, and protected, obtaining informed consent before utilizing their data in AI models. 

Upholding ethical principles and ensuring accountability 

The integration of AI into healthcare decision-making raises ethical concerns regarding transparency, accountability, and ethical use of AI algorithms. To address these concerns, healthcare organizations must: 

Transparency in AI algorithms:

Provide transparency and explainability of AI algorithms, enabling healthcare professionals to understand the rationale behind AI-generated decisions. 

Accountability for AI-generated outcomes:

Establish clear accountability mechanisms for AI-generated outcomes, ensuring that there is a process for addressing errors and potential harm. 

Ethical frameworks and guidelines:

Develop and adhere to ethical frameworks and guidelines that govern the responsible use of AI in healthcare, addressing issues such as fairness, non-discrimination, and respect for patient autonomy. 

Ensuring safe passage: A continuous commitment 

The responsible implementation of AI in healthcare requires a proactive and multifaceted approach that addresses potential risks, upholds ethical principles, and safeguards patient privacy.

By adopting these measures, healthcare organizations can harness the power of AI to transform healthcare delivery while ensuring that the benefits of AI are realized in a safe, equitable, and ethical manner. 

Unlocking the Power of LLM Use-Cases: AI applications now excel at summarizing articles, weaving narratives, and sparking conversations, all thanks to advanced large language models.

A large language model, abbreviated as LLM, represents a deep learning algorithm with the capability to identify, condense, translate, forecast, and generate text as well as various other types of content. These abilities are harnessed by drawing upon extensive knowledge extracted from massive datasets.

Large language models, which are a prominent category of transformer models, have proven to be exceptionally versatile. They extend beyond simply instructing artificial intelligence systems in human languages and find application in diverse domains like deciphering protein structures, composing software code, and many other multifaceted tasks.

Furthermore, apart from enhancing natural language processing applications such as translation, chatbots, and AI-powered assistants, large language models are also being employed in healthcare, software development, and numerous other fields for various practical purposes.

LLM use cases

Applications of large language models

Language serves as a conduit for various forms of communication. In the vicinity of computers, code becomes the language. Large language models can be effectively deployed in these linguistic domains or scenarios requiring diverse communication.

These models significantly expand the purview of AI across industries and businesses, poised to usher in a new era of innovation, ingenuity, and efficiency. They possess the potential to generate intricate solutions to some of the world’s most intricate challenges.

For instance, an AI system leveraging large language models can acquire knowledge from a database of molecular and protein structures. It can then employ this knowledge to propose viable chemical compounds, facilitating groundbreaking discoveries in vaccine and treatment development.

LLM Use-Cases: 10 industries revolutionized by large language models

Large language models are also instrumental in creating innovative search engines, educational chatbots, and composition tools for music, poetry, narratives, marketing materials, and beyond. Without wasting time, let delve into top 10 LLM use-cases:

1. Marketing and Advertising

• Personalized marketing: LLMs can be used to generate personalized marketing content, such as email campaigns and social media posts. This can help businesses to reach their target customers more effectively and efficiently. For example, an LLM could be used to generate a personalized email campaign for customers who have recently abandoned their shopping carts. The email campaign could include information about the products that the customer was interested in, as well as special offers and discounts.

• Chatbots: LLMs can be used to create chatbots that can interact with customers in a natural way. This can help businesses to provide customer service 24/7 without having to hire additional staff. For example, an LLM could be used to create a chatbot that can answer customer questions about products, services, and shipping.

• Content creation: LLMs can be used to create marketing content, such as blog posts, articles, and social media posts. This content can be used to attract attention, engage customers, and promote products and services. For example, an LLM could be used to generate a blog post about a new product launch or to create a social media campaign that encourages customers to share their experiences with the product.

• Targeting ads: LLMs can be used to target ads to specific audiences. This can help businesses to reach their target customers more effectively and efficiently. For example, an LLM could be used to target ads to customers who have shown interest in similar products or services.

• Measuring the effectiveness of marketing campaigns: LLMs can be used to measure the effectiveness of marketing campaigns by analyzing customer data and social media activity. This information can be used to improve future marketing campaigns.

• Generating creative text formats: LLMs can be used to generate different creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc. This can be used to create engaging and personalized marketing content.

Here are some other use cases for large language models in marketing and advertising:

• Content creation: LLMs can be used to create marketing content, such as blog posts, articles, and social media posts. This content can be used to attract attention, engage customers, and promote products and services.
• Measuring the effectiveness of marketing campaigns: LLMs can be used to measure the effectiveness of marketing campaigns by analyzing customer data and social media activity. This information can be used to improve future marketing campaigns.
• Targeting ads: LLMs can be used to target ads to specific audiences. This can help businesses to reach their target customers more effectively and efficiently.

2. Retail and eCommerce

A large language model can be used to analyze customer data, such as past purchases, browsing history, and social media activity, to identify patterns and trends. This information can then be used to generate personalized recommendations for products and services. For example, an LLM could be used to recommend products to customers based on their interests, needs, and budget.

Here are some other use cases for large language models in retail and eCommerce:

• Answering customer inquiries: LLMs can be used to answer customer questions about products, services, and shipping. This can help to free up human customer service representatives to handle more complex issues.
• Assisting with purchases: LLMs can be used to guide customers through the purchase process, such as by helping them to select products, add items to their cart, and checkout.
• Fraud detection: LLMs can be used to identify fraudulent activity, such as credit card fraud or identity theft. This can help to protect businesses from financial losses.

3. Education

Large language models can be used to create personalized learning experiences for students. This can help students to learn at their own pace and focus on the topics that they are struggling with. For example, an LLM could be used to create a personalized learning plan for a student who is struggling with math. The plan could include specific exercises and activities that are tailored to the student’s needs.

Answering student questions

Large language models can be used to answer student questions in a natural way. This can help students to learn more effectively and efficiently. For example, an LLM could be used to answer a student’s question about the history of the United States. The LLM could provide a comprehensive and informative answer, even if the question is open-ended or challenging.

Generating practice problems and quizzes

Large language models can be used to generate practice problems and quizzes for students. This can help students to review the material that they have learned and prepare for exams. For example, an LLM could be used to generate a set of practice problems for a student who is taking a math test. The problems would be tailored to the student’s level of understanding and would help the student to identify any areas where they need more practice.

Here are some other use cases for large language models in education:

• Grading student work: LLMs can be used to grade student work, such as essays and tests. This can help teachers to save time and focus on other aspects of teaching.
• Creating virtual learning environments: LLMs can be used to create virtual learning environments that can be accessed by students from anywhere. This can help students to learn at their own pace and from anywhere in the world.
• Translating textbooks and other educational materials: LLMs can be used to translate textbooks and other educational materials into different languages. This can help students to access educational materials in their native language.

4. Healthcare

Large language models (LLMs) are being used in healthcare to improve the diagnosis, treatment, and prevention of diseases. Here are some of the ways that LLMs are being used in healthcare:

• Medical diagnosis: LLMs can be used to analyze medical records and images to help diagnose diseases. For example, an LLM could be used to identify patterns in medical images that are indicative of a particular disease.
• Patient monitoring: LLMs can be used to monitor patients’ vital signs and other health data to identify potential problems early on. For example, an LLM could be used to track a patient’s heart rate and blood pressure to identify signs of a heart attack.
• Drug discovery: LLMs can be used to analyze scientific research to identify new drug targets and to predict the effectiveness of new drugs. For example, an LLM could be used to analyze the molecular structure of a disease-causing protein to identify potential drug targets.
• Personalized medicine: LLMs can be used to personalize treatment plans for patients by taking into account their individual medical history, genetic makeup, and lifestyle factors. For example, an LLM could be used to recommend a specific drug to a patient based on their individual risk factors for a particular disease.
• Virtual reality training: LLMs can be used to create virtual reality training environments for healthcare professionals. This can help them to learn new skills and to practice procedures without putting patients at risk.

5. Finance

Large language models (LLMs) are being used in finance to improve the efficiency, accuracy, and transparency of financial markets. Here are some of the ways that LLMs are being used in finance:

• Financial analysis: LLMs can be used to analyze financial reports, news articles, and other financial data to help financial analysts make informed decisions. For example, an LLM could be used to identify patterns in financial data that could indicate a change in the market.
• Risk assessment: LLMs can be used to assess the risk of lending money to borrowers or investing in a particular company. For example, an LLM could be used to analyze a borrower’s credit history and financial statements to assess their risk of defaulting on a loan.
• Trading: LLMs can be used to analyze market data to help make improved trading decisions. For example, an LLM could be used to identify trends in market prices and to predict future price movements.
• Fraud detection: LLMs can be used to detect fraudulent activity, such as money laundering or insider trading. For example, an LLM could be used to identify patterns in financial transactions that are indicative of fraud.
• Compliance: LLMs can be used to help financial institutions comply with regulations. For example, an LLM could be used to identify potential violations of anti-money laundering regulations.

6. Law

Technology has greatly transformed the legal field, streamlining tasks like research and document drafting that once consumed lawyers’ time.

• Legal research: LLMs can be used to search and analyze legal documents, such as case law, statutes, and regulations. This can help lawyers to find relevant information more quickly and easily. For example, an LLM could be used to search for all cases that have been decided on a particular legal issue.
• Document drafting: LLMs can be used to draft legal documents, such as contracts, wills, and trusts. This can help lawyers to produce more accurate and consistent documents. For example, an LLM could be used to generate a contract that is tailored to the specific needs of the parties involved.
• Legal analysis: LLMs can be used to analyze legal arguments and to identify potential weaknesses. This can help lawyers to improve their legal strategies. For example, an LLM could be used to analyze a precedent case and to identify the key legal issues that are relevant to the case at hand.
• Litigation support: LLMs can be used to support litigation by providing information, analysis, and insights. For example, an LLM could be used to identify potential witnesses, to track down relevant evidence, or to prepare for cross-examination.
• Compliance: LLMs can be used to help organizations comply with regulations by identifying potential violations and providing recommendations for remediation. For example, an LLM could be used to identify potential violations of anti-money laundering regulations.

Read more –> LLM for Lawyers, enrich your precedents with the use of AI

7. Media

The media and entertainment industry embraces a data-driven shift towards consumer-centric experiences, with LLMs poised to revolutionize personalization, monetization, and content creation.

• Personalized recommendations: LLMs can be used to generate personalized recommendations for content, such as movies, TV shows, and news articles. This can be done by analyzing user preferences, consumption patterns, and social media signals.
• Intelligent content creation and curation: LLMs can be used to generate engaging headlines, write compelling copy, and even provide real-time feedback on content quality. This can help media organizations to streamline content production processes and improve overall content quality.
• Enhanced engagement and monetization: LLMs can be used to create interactive experiences, such as interactive storytelling and virtual reality. This can help media organizations to engage users in new and innovative ways.
• Targeted advertising and content monetization: LLMs can be used to generate insights that inform precise ad targeting and content recommendations. This can help media organizations to maximize ad revenue.

Bigwigs with LLM – Netflix uses LLMs to generate personalized recommendations for its users. The New York Times uses LLMs to write headlines and summaries of its articles. The BBC uses LLMs to create interactive stories that users can participate in. Spotify uses LLMs to recommend music to its users.

8. Military

• Synthetic training data: LLMs can be used to generate synthetic training data for military applications. This can be used to train machine learning models to identify objects and patterns in images and videos. For example, LLMs can be used to generate synthetic images of tanks, ships, and aircraft.
• Natural language processing: LLMs can be used to process natural language text, such as reports, transcripts, and social media posts. This can be used to extract information, identify patterns, and generate insights. For example, LLMs can be used to extract information from a report on a military operation.
• Machine translation: LLMs can be used to translate text from one language to another. This can be used to communicate with allies and partners, or to translate documents and media. For example, LLMs can be used to translate a military briefing from English to Arabic.
• Chatbots: LLMs can be used to create chatbots that can interact with humans in natural language. This can be used to provide customer service, answer questions, or conduct research. For example, LLMs can be used to create a chatbot that can answer questions about military doctrine.
• Cybersecurity: LLMs can be used to detect and analyze cyberattacks. This can be used to identify patterns of malicious activity, or to generate reports on cyberattacks. For example, LLMs can be used to analyze a network traffic log to identify a potential cyberattack.

9. HR

• Recruitment: LLMs can be used to automate the recruitment process, from sourcing candidates to screening resumes. This can help HR teams to save time and money and to find the best candidates for the job.
• Employee onboarding: LLMs can be used to create personalized onboarding experiences for new employees. This can help new employees to get up to speed quickly and feel more welcome.
• Performance management: LLMs can be used to provide feedback to employees and to track their performance. This can help managers to identify areas where employees need improvement and to provide them with the support they need to succeed.
• Training and development: LLMs can be used to create personalized training and development programs for employees. This can help employees to develop the skills they need to succeed in their roles.
• Employee engagement: LLMs can be used to survey employees and to get feedback on their work experience. This can help HR teams to identify areas where they can improve the employee experience.

Here is a specific example of how LLMs are being used in HR today: The HR company, Mercer, is using LLMs to automate the recruitment process. This is done by using LLMs to screen resumes and to identify the best candidates for the job. This has helped Mercer to save time and money and to find the best candidates for their clients.

10. Fashion

How LLMs are being used in fashion today? The fashion brand, Zara, is using LLMs to generate personalized fashion recommendations for its users. This is done by analyzing user data, such as past purchases, social media activity, and search history. This has helped Zara to improve the accuracy and relevance of its recommendations and to increase customer satisfaction.

• Personalized fashion recommendations: LLMs can be used to generate personalized fashion recommendations for users based on their style preferences, body type, and budget. This can be done by analyzing user data, such as past purchases, social media activity, and search history.
• Trend forecasting: LLMs can be used to forecast fashion trends by analyzing social media data, news articles, and other sources of information. This can help fashion brands to stay ahead of the curve and create products that are in demand.
• Design automation: LLMs can be used to automate the design process for fashion products. This can be done by generating sketches, patterns, and prototypes. This can help fashion brands to save time and money, and to create products that are more innovative and appealing.
• Virtual try-on: LLMs can be used to create virtual try-on experiences for fashion products. This can help users to see how a product would look on them before they buy it. This can help to reduce the number of returns and improve the customer experience.
• Customer service: LLMs can be used to provide customer service for fashion brands. This can be done by answering questions about products, processing returns, and resolving complaints. This can help to improve the customer experience and reduce the workload on customer service representatives.

Wrapping up

In conclusion, large language models (LLMs) are shaping a transformative landscape across various sectors, from marketing and healthcare to education and finance. With their capabilities in personalization, automation, and insight generation, LLMs are poised to redefine the way we work and interact in the digital age. As we continue to explore their vast potential, we anticipate breakthroughs, innovation, and efficiency gains that will drive us toward a brighter future.

Healthcare is a necessity for human life, yet many do not have access to it. Here are 10 startups that are using AI to change healthcare.

Healthcare is a necessity that is inaccessible to many across the world. Despite rapid developments and improvements in medical research, healthcare systems have become increasingly unaffordable.

However, multiple startups and tech companies have been trying their best to integrate AI and machine learning for improvements in this sector.

As the population of the planet increases along with life expectancy due to advancements in agriculture, science, medicine, and more, the demand for functioning healthcare systems also rises.

According to McKinsey & Co., by the year 2050, in Europe and North America, 1 in 4 people will be over the age of 65 Source). Healthcare systems by that time will have to manage numerous patients with complex needs.

Read about Top 15 AI startups developing financial services in the USA

Here is a list of a few Artificial Intelligence (AI) startups that are trying their best to revolutionize the healthcare industry as we know it today and help their fellow human beings:

1. Owkin aims to find the right drug for every patient.

Originating in Paris, France, Owkin was launched in 2016 and develops a federated learning AI platform, that helps pharmaceutical companies discover new drugs, enhance the drug development process, and identify the best drug for the ‘right patient.’ Pretty cool, right?

Owkin makes use of different machine learning models to test AI models on distributed data.

The startup also aims to empower researchers across hospitals, educational institutes, and pharmaceutical companies to understand why drug efficacy varies from patient to patient.

2. Overjet is providing accurate data for better patient care and disease management.

Founded by PhDs from the Massachusetts Institute of Technology and dentists from Harvard School of Dental Medicine in 2018, Overjet is changing the playground in dental AI.

Overjet makes use of AI to make use of dentist-level understanding of the subject for the identification of diseases and their progression into software.

Overjet aims to provide effective and accurate data to dentists, dental groups, and insurance companies so that they can provide the best patient care and disease management.

3. From the mid-Atlantic health system to an enterprise-wide AI workforce, Olive AI is improving operational healthcare efficiency.

Founded in 2012, Olive AI is the only known AI as a Service (AIaaS) built for the healthcare sector. The premier AI startup utilizes the power of cloud computing by implementing Amazon Web Services (AWS) and automating systems that accelerate time to care.

With more than 200 enterprise customers such as health systems, insurance companies, and a growing number of healthcare companies. Olive AI assists healthcare workers with time-consuming tasks like prior authorizations and patient verifications.

Find out more about Olive AI, click here.

Want to learn more about AI as a Service? Click here.

4. Insitro provides better medicines for patients with the overlap of biology and machine learning.

The perfect cross between biology and machine learning, Insitro aims to support pharmaceutical research and development, and improve healthcare services. Founded in 2018, Insitro promotes Machine Learning-Based Drug Discovery for which it has raised a substantial amount of funding over the years.

According to a recent Forbes ranking of the top 50 AI businesses, the HealthTech startup is ranked at 35 for having the most promising AI-based medication development process.

5. Caption Health makes early disease detection easier.

Founded in 2013, Caption Health has since been a top provider of medical artificial intelligence. The startup is responsible for the early identification of illnesses.

Caption Health was the first to provide the FDA-approved AI imaging and guiding software for cardiac ultrasonography. The startup has helped remove numerous barriers to treatment and enabled a wide range of people to perform heart scans of diagnostic quality.

6. InformAI is trying to transform the way healthcare is delivered and improve patient outcomes.

Founded in 2017, InformAI expedites medical diagnosis while increasing the productivity of medical professionals.

Focusing on AI and deep learning, as well as business analytics solutions for hospitals and medical companies, InformAI was built for AI-enabled medical image classification, healthcare operations, patient outcome predictors, and much more.

InformAI not only has top-tier medical professionals at its disposal, but also has 10 times more access to proprietary medical datasets, as well as numerous AI toolsets for data augmentation, model optimization, and 3D neural networks.

7. Recursion is decoding biology to improve lives across the globe.

A biotechnology startup, Recursion was founded in 2013 and focuses on multiple disciplines, ranging from biology, chemistry, automation, and data science, to even engineering.

Recursion focuses on creating one of the largest and fastest-growing proprietary biological and chemical datasets in the world.

8. Remedy Health provides information and insights for better navigation of the healthcare industry.

As AI advances, so does the technology that powers it. Another marvelous startup known as Remedy Health is allowing people to conduct phone screening interviews with clinically skilled professionals to help identify hidden chronic conditions.

The startup makes use of virtual consultations, allowing low-cost, non-physician employees to proactively screen patients.

To learn more about Remedy Health, click here.

9. Sensely is transforming conversational AI.

Founded in 2013, Sensely is an avatar and chatbot-based platform that aids insurance plan members and patients.

The startup provides virtual assistance solutions to different enterprises including insurance and pharmaceutical companies, as well as hospitals to help them converse better with their members.

Sensely’s business ideology can further be explored here.

10. Oncora Medical provides a one-stop solution for oncologists.

Another digital health company, founded in 2014, Oncora Medical focuses on creating a crossover between data and machine learning for radiation oncology.

The main aim of the startup was to create a centralized platform for better collection and application of real-world data that can in some way help patients.

With the international AI in the healthcare market expected to reach over USD 36B by the year 2025, it is only accurate to expect that this market and specific niche will continue to grow even further.

If you would like to learn more about Artificial Intelligence, click here.

Was there any AI-based healthcare startup that we missed? Let us know in the comments below. For similar listicles, click here.

Written by Alyshai Nadeem

In this blog, we discussed the applications of AI in healthcare. We took a deep dive into an application of AI, and prognosis prediction using an exercise. We made a simple prognosis detector with an explanation of each step. Our predictor takes symptoms as inputs and predicts the prognosis using a classification model.

The role of data science and AI (Artificial Intelligence) in the Healthcare industry is not limited to predicting and tracking disease spread. Now, it has become possible to learn the causes of whatever symptoms you are experiencing, such as cough, fever, and body pain, without visiting a doctor and self-treating it at home. Platforms like Ada Health and Sensely can diagnose the symptoms you report.

If you have not already, please go back and read AI & Healthcare. If you have already read it, you will remember I wrote, “Predictive analysis, using historical data to find patterns and predict future outcomes can find the correlation between symptoms, patients’ habits, and diseases to derive meaningful predictions from the data.”

This tutorial will do just that: Predict the prognosis with symptoms as our input.

Exercise: Predict prognosis using symptoms as input

Let us start by importing all the libraries needed in the exercise. We import pandas as we will be reading CSV files as Data Frame. We are importing Label Encoder from sklearn.preprocessing package. Label Encoder is a utility class to convert non-numerical labels to numerical labels. In this exercise, we predict prognosis using symptoms, so it is a classification task.

We are using RandomForestClassifier, which consists of many individual decision trees that work as an ensemble. Learn more about RandomForestClassifier by enrolling in our Data Science Bootcamp, a remote instructor-led Bootcamp. We also require classification reports and accuracy score metrics to measure the model’s performance.

import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score

Read CSV files

We are using this Kaggle dataset for our exercise.

It has two files, Training.csv and Testing.csv, containing training and testing data, respectively. You can download these files by going to the data section of the above link.

Read CSV files into Data Frame using pandas read_csv() function. It reads comma-separated files at supplied file path into DataFrame. It takes a file path as a parameter, so provide the right file path where you have downloaded the files.

train = pd.read_csv("File path of Training.csv")
test = pd.read_csv("File path of Testing.csv")

Check samples of the training dataset

To check what the data looks like, let us grab the first five rows of the DataFrame using the head() function.

We have 133 features. We want to predict prognosis so that it would be our target variable. The rest of the 132 features are symptoms that a person experience. The classifier would use these 132 symptoms feature to predict prognosis.

train.head()

The training set holds 4920 samples and 133 features, as shown by the shape attribute of the DataFrame.

train.shape

Output
(4920, 133)

Descriptive analysis

Description of the data in the DataFrame can be seen by describe() method of the DataFrame. We see no missing values in our DataFrame as the count of all the features is 4920, which is also the number of samples in our DataFrame. We also see that all the numeric features are binary and have a value of either 1 or 0.

train.describe()

train.describe(include=['object'])

Our target variable prognosis has 41 unique values, so there are 41 diseases in which the model will classify input. There are 120 samples for each unique prognoses in our dataset.

train['prognosis'].value_counts()

There are 132 symptoms in our dataset. The names of the symptoms will be listed if we use this code block.

possible_symptoms = train[train.columns.difference(['prognosis'])].columnsprint(list(possible_symptoms))

Output
['abdominal_pain', 'abnormal_menstruation', 'acidity', 'acute_liver_failure', 'altered_sensorium', 'anxiety', 'back_pain', 'belly_pain', 'blackheads', 'bladder_discomfort', 'blister', 'blood_in_sputum', 'bloody_stool', 'blurred_and_distorted_vision', 'breathlessness', 'brittle_nails', 'bruising', 'burning_micturition', 'chest_pain', 'chills', 'cold_hands_and_feets', 'coma', 'congestion', 'constipation', 'continuous_feel_of_urine', 'continuous_sneezing', 'cough', 'cramps', 'dark_urine', 'dehydration', 'depression', 'diarrhoea', 'dischromic _patches', 'distention_of_abdomen', 'dizziness', 'drying_and_tingling_lips', 'enlarged_thyroid', 'excessive_hunger', 'extra_marital_contacts', 'family_history', 'fast_heart_rate', 'fatigue', 'fluid_overload', 'fluid_overload.1', 'foul_smell_of urine', 'headache', 'high_fever', 'hip_joint_pain', 'history_of_alcohol_consumption', 'increased_appetite', 'indigestion', 'inflammatory_nails', 'internal_itching', 'irregular_sugar_level', 'irritability', 'irritation_in_anus', 'itching', 'joint_pain', 'knee_pain', 'lack_of_concentration', 'lethargy', 'loss_of_appetite', 'loss_of_balance', 'loss_of_smell', 'malaise', 'mild_fever', 'mood_swings', 'movement_stiffness', 'mucoid_sputum', 'muscle_pain', 'muscle_wasting', 'muscle_weakness', 'nausea', 'neck_pain', 'nodal_skin_eruptions', 'obesity', 'pain_behind_the_eyes', 'pain_during_bowel_movements', 'pain_in_anal_region', 'painful_walking', 'palpitations', 'passage_of_gases', 'patches_in_throat', 'phlegm', 'polyuria', 'prominent_veins_on_calf', 'puffy_face_and_eyes', 'pus_filled_pimples', 'receiving_blood_transfusion', 'receiving_unsterile_injections', 'red_sore_around_nose', 'red_spots_over_body', 'redness_of_eyes', 'restlessness', 'runny_nose', 'rusty_sputum', 'scurring', 'shivering', 'silver_like_dusting', 'sinus_pressure', 'skin_peeling', 'skin_rash', 'slurred_speech', 'small_dents_in_nails', 'spinning_movements', 'spotting_ urination', 'stiff_neck', 'stomach_bleeding', 'stomach_pain', 'sunken_eyes', 'sweating', 'swelled_lymph_nodes', 'swelling_joints', 'swelling_of_stomach', 'swollen_blood_vessels', 'swollen_extremeties', 'swollen_legs', 'throat_irritation', 'toxic_look_(typhos)', 'ulcers_on_tongue', 'unsteadiness', 'visual_disturbances', 'vomiting', 'watering_from_eyes', 'weakness_in_limbs', 'weakness_of_one_body_side', 'weight_gain', 'weight_loss', 'yellow_crust_ooze', 'yellow_urine', 'yellowing_of_eyes', 'yellowish_skin']

There are 41 unique prognoses in our dataset. The name of all prognoses will be listed if we use this code block:

list(train['prognosis'].unique())

Output
['Fungal infection','Allergy','GERD','Chronic cholestasis','Drug Reaction','Peptic ulcer diseae','AIDS','Diabetes ','Gastroenteritis','Bronchial Asthma','Hypertension ','Migraine','Cervical spondylosis','Paralysis (brain hemorrhage)','Jaundice','Malaria','Chicken pox','Dengue','Typhoid','hepatitis A','Hepatitis B','Hepatitis C','Hepatitis D','Hepatitis E','Alcoholic hepatitis','Tuberculosis','Common Cold','Pneumonia','Dimorphic hemmorhoids(piles)','Heart attack','Varicose veins','Hypothyroidism','Hyperthyroidism','Hypoglycemia','Osteoarthristis','Arthritis','(vertigo) Paroymsal  Positional Vertigo','Acne','Urinary tract infection','Psoriasis','Impetigo']

Data visualization

new_df = train[train.columns.difference(['prognosis'])]
#Maximum Symptoms present for a Prognosis are 17
new_df.sum(axis=1).max()
Minimum Symptoms present for a Prognosis are 3
new_df.sum(axis=1).min()
series = new_df.sum(axis=0).nlargest(n=15)
pd.DataFrame(series, columns=["Occurance"]).loc[::-1, :].plot(kind="barh")

Fatigue and vomiting are the symptoms most often seen.

Encode object prognosis

Our target variable is categorical features. Let us create an instance of Label Encoder and fit it with the prognosis column of train data and test data. It will encode all possible categorical values in numerical values.

label_encoder = LabelEncoder()
label_encoder.fit(pd.concat([train['prognosis'], test['prognosis']]))

It concludes the data preparation step. Now, we can move on to model training with this data.

Training and evaluating model

Let us train a RandomForestClassifier with the prepared data. We initialize RandomForestClassifier, fit the features and label in it then finally make a prediction on our test data.

In the end, we transform label encoded prognosis values back to the original form using the fit_transform() method of the LabelEncoder object.

random_forest = RandomForestClassifier()
random_forest.fit(train[train.columns.difference(['prognosis'])], label_encoder.fit_transform(train['prognosis']))
y_pred = random_forest.predict(test[test.columns.difference(['prognosis'])])
y_true = label_encoder.fit_transform(test['prognosis'])
print("Accuracy:", accuracy_score(y_true, y_pred))
print(classification_report(y_true, y_pred, target_names=test['prognosis']))

Predict prognosis by taking symptoms as input

We have our model trained and ready to make predictions. We need to create a function that takes symptoms as input and predicts the prognosis as output. The function predict_prognosis() below is just doing that.

We take input features as a string of symptoms separated by space. We strip the string to remove spaces at the beginning and end of the string. We split this string and created a list of symptoms. We cannot use this list directly in the model for prediction as it contains symptoms’ names, but our model takes a list of 0 and 1 for the absence and presence of symptoms. Finally, with the features in the desired form, we predict the prognosis and print the predicted prognosis.

def predict_prognosis():
  print("List of possible Symptoms you can enter: ", list(train[train.columns.difference(['prognosis'])].columns))
  input_symptoms = list(input("\nEnter symptoms space separated: ").strip().split())
  print(input_symptoms)
  test_value = []
  for symptom in train[train.columns.difference(['prognosis'])].columns:
    if symptom in input_symptoms:
      test_value.append(1)
    else:
      test_value.append(0)
    np_test = np.array(test_value).reshape(1, -1)
    encoded_label = random_forest.predict(np_test)
  predicted_label = label_encoder.inverse_transform(encoded_label)[0]
  print("Predicted Prognosis: ", predicted_label)
predict_prognosis()

Give input symptoms:

Predicted prognoses

Conclusion

To sum up, we discussed the applications of AI in healthcare. Took a deep dive into an application of AI, and prognosis prediction using an exercise. Created a prognosis predictor with an explanation of each step. Finally, we tested our predictor by giving it input symptoms and got the prognosis as output.

Data science in healthcare is the next big step in the field for doctors. It, therefore, makes sense that doctors must have some knowledge of data science.

We have entered into the era of big data where we are not only using every bit of data originating from every source but also making smart decisions that accelerate business growth.

No matter what industry you’re in, AI & Big Data are all the rage these days, and the need for storing data has grown over the years. The following post emphasizes why healthcare professionals should learn data science.

It’s said – Health is wealth; with great Health, you can conquer it all! The medicine and healthcare industries are considered as one of the most revolutionary and promising industries around.

Slowly and steadily things are changing from computerizing medical records to drug discovery, and genetic disease exploration; one can find data analytics moving medical science to a whole new level. And trust me, the fun has just begun!

Data Science in Healthcare

Healthcare and data science are often linked as increased industries tend to attempt to reduce their expenses with the help of data. Day in, day out, the field of data science in medicine is developing on a rapid basis and it’s important they keep on marching together.

In general, data scientists are the ones who have advanced training in statistics, math, and computer science. Data visualization, data mining, and information management are some of its crucial aspects.

Businesses have already jumped at the opportunity produced by the combination of data science and healthcare. For example, Omada Health developed a program aimed at reducing the risk of preventable health issues.

It takes data from smart devices, like scales and pedometers, to process the patient’s behavioral data, and develops a highly customized program based on the results.

Another Example is produced by Enlitic, an organization that pairs radiologists with data scientists to increase the accuracy and efficiency of diagnostics. Featuring deep learning algorithms, data scientists help analyze data from CT scans, X-rays, etc. so that doctors can “diagnose sooner with renowned accuracy”.

A few skills required to make out a career as a healthcare data scientist include:

• Dimensionality Reduction
• Supervised Machine Learning
• Time Series Analysis
• Natural Language Processing

Furthermore, I would like to mention some interesting use cases of data science with the highest impact and the most significant potential for the future in the healthcare realm.

1. Medical image analysis – If we look at the overall healthcare sector, you will find that it has received a great bunch of benefits from data science applications. Several techniques, including magnetic resonance imaging (MRI), X-ray, computed tomography, and mammography, are used for better treatment.
2. Whether it’s about improving the image quality or extracting data from images more efficiently and providing the most accurate interpretation, data science seems to, and will continue to, contribute to a great extent.
3. Genetics and Genomics – This feature has enabled an advanced level of treatment personalization.
4. Professionals need to understand the impact of DNA on our Health and find individual biological connections between genetics, diseases, and drug response. Data science techniques allow for the integration of various kinds of data with genomic data in disease research. This provides a deeper understanding of how different genetic structures will react to drugs and diseases.
5. Virtual assistance – Optimization of the clinical process builds upon the concept that, for many cases, it is not actually necessary for patients to visit doctors in person. The mobile application can give a more effective solution by “bringing the doctor to the patient”.  AI-powered mobile apps can provide basic healthcare support, usually as chatbots.
6. All you have to do is describe your symptoms, or ask questions, and then receive key information about your medical condition. Apps can remind you to take your medicine on time, and if necessary, assign an appointment with a doctor.