Resources - Course

Introduction

The UT Austin online master's degree in AI prepares you to stand out in this fast-growing field through one of the first AI master's programs available 100% online.

Dr. Ding's AI In Healthcare Courses

This course explores the major components of health IT systems, ranging from data semantics (ICD10), data interoperability (FHIR), diagnosis code (SNOMED CT), to workflow in clinical decision support systems. Then, it dives deep into how AI innovations are transforming our healthcare system by focusing on AI in drug discovery, AI in medical image diagnosis, explainable AI for health risk prediction, and ethics of AI in healthcare.

What You Will Learn

Be aware of current healthcare initiatives to deliver quality care

Understand the technologies underlying health IT systems, including data semantics, data interoperability, workflow, and clinical decision support systems

Deepen understanding of electronic health record systems (EHR systems)

Gain a broad overview of AI innovations in healthcare

Master practical skills of data search and analytics including database search, natural language processing, data visualization, machine learning, and deep learning

Syllabus

Evidence-based Care, i2b2 and OMOP

EMR Semantics: ICD10, ICD10 (COVID) and ICD9 (MIMIC)

EMR Semantics: SNOMED CT I

EMR Semantics: SNOMED CT II, SNOMED and ICD10

EMR Semantics: LOINC

EMR Semantics: RxNorm

Clinical Decision Support System

Data Share: FHIR

AI health: ML/DL I (Explainable AI and Multimodal fusion learning)

AI health: ML/DL II Advanced Medical NLP

AI health: imaging (Medical Imaging Diagnosis)

AI in Drug Discovery

Ethics of AI in Health