Advanced AI in Clinical Analytics

Course Overview

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Mentor Based

Program Syllabus

Module 1

About This Course

The Advanced AI in Clinical Analytics program is tailored for professionals seeking to lead the integration of AI into clinical settings. This course provides comprehensive training in the latest AI methodologies for analyzing complex medical data sets, designing predictive models, and implementing AI-driven decision support systems in healthcare.

Module 2

Aim

This program aims to equip PhD scholars and academicians with the skills to apply advanced AI techniques to clinical analytics, transforming the way healthcare data is analyzed and used for patient care. It focuses on developing expertise in leveraging AI to uncover deep insights from clinical data, optimizing healthcare delivery, and improving patient outcomes.

Module 3

Program Objectives

Master advanced AI techniques to enhance clinical data analysis.
Develop predictive models that improve patient care and clinical outcomes.
Implement AI-driven decision support systems in real-world healthcare settings.
Navigate the ethical and regulatory issues surrounding clinical AI.
Lead interdisciplinary teams in the adoption and integration of AI technologies in healthcare.

Module 4

Program Structure

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.

Week 1: Clinical Data Foundations and Modeling Approaches
Module 1: Foundations of Clinical Data Science

Chapter 1.1: Clinical Data Types – EHRs, Lab Data, Imaging, Claims
Chapter 1.2: Data Quality, Standardization (FHIR, HL7), and Governance
Chapter 1.3: Data Preprocessing for AI: Missingness, Imputation, Labeling
Chapter 1.4: Ethical and Legal Considerations in Clinical Data Use

Module 2: Predictive Modeling in Healthcare

Chapter 2.1: Risk Prediction Models (Readmission, Mortality, LOS)
Chapter 2.2: Feature Engineering for Clinical Use Cases
Chapter 2.3: Handling Longitudinal and Time-Series Clinical Data
Chapter 2.4: Evaluation Metrics: AUROC, Precision/Recall, Calibration

Week 2: Deep Learning, NLP, and Imaging in Clinical Contexts
Module 3: Advanced Modeling Techniques

Chapter 3.1: Deep Neural Networks for Structured EHR Data
Chapter 3.2: Time-Series Models: RNNs, LSTMs, Transformers in Healthcare
Chapter 3.3: Multi-modal Models: Combining Text, Labs, and Images
Chapter 3.4: Transfer Learning and Pretrained Models in Clinical Tasks

Module 4: Clinical NLP and Imaging AI

Chapter 4.1: Information Extraction from Clinical Notes
Chapter 4.2: Named Entity Recognition and ICD Code Prediction
Chapter 4.3: Clinical Imaging Models: Radiology, Pathology, Ophthalmology
Chapter 4.4: Annotating and Validating NLP/Imaging Models

Week 3: Deployment, Fairness, and Real-World Integration
Module 5: AI Deployment in Health Systems

Chapter 5.1: Integrating Models into Clinical Workflows
Chapter 5.2: Model Monitoring, Drift Detection, and Retraining
Chapter 5.3: Clinical Decision Support and User Interface Design
Chapter 5.4: Validation in Multi-Site and Real-World Settings

Module 6: Fairness, Interpretability, and Governance

Chapter 6.1: Bias in Clinical AI: Causes, Detection, and Mitigation
Chapter 6.2: Explainability in Clinical Predictions
Chapter 6.3: Regulatory Pathways: FDA, HIPAA, and AI Oversight
Chapter 6.4: Capstone – Design a Clinical AI Model Pipeline with Governance Plan