Spring 2024

INF 385T Special Topics in Information Science: AI in Health

Unique ID: 27715


09:30 AM - 12:30 PM  UTA 1.208


Exploration of 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. After establishing a good understanding of the fundamentals of health IT systems, we will dive deep into how AI innovations (e.g., machine learning, deep learning, computer vision) are transforming our healthcare system by introducing new concepts of mobile health, AI diagnosis, AI medicine, smart device, and intelligent delivery.


This course will offer hands-on tutorials based on the real-world Electronic Health Record (EHR) data from MIMIC III (https://mimic.physionet.org/) released by MIT Critical Data. MIMIC-III (Medical Information Mart for Intensive Care III) contains de-identified health information from over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. These tutorials aim to enhance data search and analytic skills by providing practices related to database search, natural language processing, data visualization, machine learning, and deep learning. In this course, we will enhance the group learning experience and learning by doing, therefore, there will be many class activities. This course is designed for everyone, so no programming background is required or desired. After completing this course, students should be able to achieve the following goals: - Be aware of current healthcare initiatives to deliver quality care - Understand the basic technologies of health IT systems including data semantics, data interoperability, workflow, and clinical decision support systems - Be familiar with electronic health record systems (EHR systems) - Gain the 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


Graduate standing.


Restricted to graduate students in the School of Information through registration periods 1 and 2. Outside students will be permitted to join our waitlists beginning with registration period 3.