Course curriculum

    1. Welcome and Introductory Instructions

    2. How to Use this Course

    3. Course Syllabus

    4. Course Competencies

    1. Module Description

    2. Pre-Reading 1: Looking Forward to AI and Medicine: Where Are We, and Where Are We Going?

    3. Pre-Reading 2: Artificial Intelligence—From Starting Pilots to Scalable Privilege

    4. Pre-Reading 3: Leveraging GPT-4 for Identifying Cancer Phenotypes in Electronic Health Records

    5. Pre-Reading 4: How Do You Test AI in Medicine

    6. Module 1: Live Call

    1. Module Description

    2. Video Module 2a- Data Science Pipeline

    3. Video Module 2b - Descriptive Analysis

    4. Video Module 2c - Data Information Quality

    5. Pre-Reading 1: How to Clean Data in Microsoft Excel: A Step-by-Step Illustrated Guide

    6. Pre-Reading 2: Top Ten Ways to Clean your Data

    7. Pre-Reading 3: Fundamentals of Research Data and Variables: The Devil Is in the Details

    8. Pre-Reading 4: Basic Introduction to Statistics in Medicine, Part 1: Describing Data

    9. Module 2: Live Call

    1. Module Description

    2. Pre-Reading 1: Meeting the Artificial Intelligence Needs of U.S. Health Systems

    3. Pre-Reading 2: Respiratory Support Status from EHR Data for Adult Population: Classification, Heuristics, and Usage in Predictive Modeling

    4. Tutorial Videos 1: Predictive Modeling

    5. Tutorial Videos 2: Clustering

    6. Module 3: Live Call

    1. Module Description

    2. Session 4 Reading 1

    3. Session 4 Reading 2

    4. Module 4: Live Call

About this course

  • Free
  • 29 lessons
  • 1 hour of video content

This course will teach you how to:

  • Integrate advanced AI models into the data science lifecycle.

  • Learn how to select and use data science tools effectively.

  • Identify and understand the best data sources for various AI applications.

  • Select and apply the most effective strategies for implementing large language models (LLM) models in health research practices.

  • Learn about processes behind AI funding mechanisms, and to secure funding for AI-related projects.

Instructor(s)

Assistant Professor Aditi Gupta

Dr. Aditi Gupta, is an Instructor in the Washington University Institute for Informatics and the Division of Biostatistics. Her core areas of interest are biomedical data science and clinical research informatics. Her research interests are analyzing and implementing informatics-based methodologies such as multi-scale and predictive modeling for identification of precision diagnostic and therapeutic strategies in cancer and other diseases.

Director, Institute for Informatics, Data Science and Biostatistics Philip Payne

Dr. Payne is an internationally recognized leader in the field of clinical research informatics (CRI) and translational bioinformatics (TBI). His research portfolio is actively supported by a combination of NCATS, NLM, and NCI grants and contracts, as well as a variety of awards from both nonprofit and philanthropic organizations.

Professor of Medicine and the Director of the Center for Applied Clinical Informatics Adam Wilcox

Dr. Wilcox is a Professor of Medicine and the Director of the Center for Applied Clinical Informatics, Institute for Informatics, Washington University in St. Louis School of Medicine. He has broad experience in both applied and research informatics, both in academia and healthcare delivery organizations. He leads strategy and activities related to application of informatics tools and methods to improve clinical care and research. Nationally, he is noted for his work with designing, developing and sustaining data systems for populations with research and electronic health record data; for design and implementation of health information systems; and for advancing methods in sustainability of data systems.