GenAI and ML in Healthcare

Course Modules

We have four modules. The first one is Biostatistics. Biostatistics (sometimes referred to as biometry) is a branch of statistics that applies statistical methods to a wide range of topics in the biological sciences, with a focus on clinical medicine and public health applications.

Machine Learning and Deep Learning are being used extensively in health sector. Deep Learning techniques are especially useful for image datasets, for example, chest-X-rays and CAT-Scans. These techniques are also used with sensor data (for example, ECG). If tabular data is sufficiently large, deep learning techniques can also be applied for making predictions.

Generative AI and LLM are being developed as Clinical Decision Support Systems (CDSSs): AI-generated CDSSs can provide clinicians with real-time insights to inform treatment decisions and improve patient outcomes.

These modules are totally hands-on, and practise based. These are online, live, and totally interactive lab-oriented Modules with the primary objective of disseminating techniques of Healthcare Analytics using Data Visualization, Machine Learning, Deep learning and Gen AI.

Types of Questions Answered

Here are several typical or atypical questions that we strive to answer in our program. We will perform Segmentation analysis, Classification analysis, Regression analysis and use Gen AI for CDSS. (Students are welcome to bring their own sample data for analysis)

Clinical Problems

• Classify fetal health in order to prevent child and maternal mortality
• Predict lung function decline—Pulmonary Fibrosis Progression
• Predict Possibility of Heart Attack
• Classify Pulmonary Embolism cases in chest CT scans
• Predict the onset of diabetes based on diagnostic measures
• Predict Age from X-rays
• Predict if an infant is likely to develop autistic tendencies
• Predict severity of epileptic seizure
• Detect Malaria through Infected Cell Images
• Detect Autism from a facial image
• Identify acute intracranial haemorrhage and its subtypes
• MRI Imaging Comparisons of Demented and Nondemented Adults
• Create an accurate model to predict the stage of Alzheimer.
• Distinguishing Different Stages of Parkinson’s Disease

Pharmaceutical and R&D Problems

• Predict a biological response of molecules from their chemical properties

Patient behaviour related

• Can you predict if a patient will keep his appointment?
• Prevalence and attitudes towards mental health among tech workers

Insurance and Costs related

• Can you accurately predict medical insurance costs? • Healthcare Provider Fraud Detection Analysis • Explore Health Insurance Marketplace • Predict length of stay in hospital • Predict medical insurance costs • Predict hospital readmission for diabetes patients

Retail purchases and sale

• Forecast sales of drugs using store, promotion, and competitor data

Develop Clinical Decision Support Systems

• Develop CDSS in any field in medical sciences using modern day research

Who Should Attend?

This program is designed for professionals and students looking to apply analytics and AI bridge the gap between Healthcare and Technology.

Life Sciences & Healthcare

Students : Biology, Biotechnology, Biochemistry, Bioinformatics, Cell Biology, Ecology, Molecular Biology, Microbiology, and Marine Sciences.

Clinical Practitioners : Doctors, Medical Practitioners, Nurse Practitioners, and Healthcare Workers.

Specialists : Bio-technologists and Bio-informatics experts.

Management & Technology

Leadership :Helping them to optimize operations (staffing, patient flow), predicting needs (readmissions, outbreaks), personalizing care, detecting fraud, and improving clinical decisions

Technical Experts : : Data Scientists, Programmers, and Engineers Analysing massive datasets (genomic, clinical, real-world) to speed up drug discovery, create personalized medicine, optimize clinical trials and improve diagnostics.

Academic Sector : Researchers and those in Academics.

Remote access to GPU equipped lab

Student access our powerful, GPU equipped lab-machines remotely (at no extra fees). When a student works remotely, the experience is as if he is working on his own laptop. Of course, a good Internet connection is desired.

Further, if a student has a laptop or desktop with minimum 16gb of RAM with at least i5 processor, we provide help to install all requisite software in his laptop. This enables a student to store all data and applications in his laptop and these will be available to him even after the course finishes.

No-code Program

We would like to highlight that we cover Biostatistics, ML, DL & Gen AI techniques using ‘No-code’ approach. We use the best, highly reputed and industry standard Graphical User Interfaces (Visual frameworks) that generally use drag-and-drop approach to build simple to complex workflows—to create data pipelines, build models, test them and then deploy for production use. All these tools/platforms are open source, have very liberal licencing policies and can be utilised even with very large data in production environment.

We use the following Visual frameworks to perform analytics, create statistical and predictive models and deploy production environment.them for use in

Software	Applied In
JASP & KNIME	Biostatistics
KNIME	Machine Learning & Deep Learning
H2O	Machine Learning & Deep Learning
Flowise	Generative AI Apps
N8N	Generative AI & Automation and Agentic Solutions
Ollama & Xinference	Generative AI: Open source platforms for Large Language models
RAGFlow	Agentic AI: Building a superior context layer for AI agents

For more details, please refer to the program brochure.