EM1901D "Bringing Machine Learning to the Bedside: Focusing Clinical Decision Support with Predictive Modeling" (IM GR-011819)

Purpose & Overview

  1. Machine learning algorithms segment or sort complex groups. They are in most cases divided into three major groups: supervised, semi-supervised, and unsupervised models.
  2. These systems raise ethical questions about their source data, biases, and implementation.
  3. Appropriate use of algorithms in clinical decision support can create enormous value and help guide resources to where they can be used best.
  4. Medical professionals must lead the effort to incorporate these models into clinical care in a safe and moral manner. The synergy between clinicians and machine learning will create enormous value to patients and the health system.

PDF icon Download Protocol

Target Audience

UT Southwestern faculty, fellows, residents and medical students, community physicians, nurse clinicians, physician assistants and nurses.

Learning Objectives

At the conclusion of this activity, the participant should be able to:

  • Identify various machine learning algorithms and how they are organized.
  • Understand how to assess quality of machine learning algorithms and the models created by them.
  • Describe approaches to implement these methods in real-time clinical decision support.
Course summary
Available credit: 
  • 1.00 AMA
Course opens: 
Course expires: 

Photo: Mujeeb Basit, M.D.Mujeeb Basit, M.D.
Assistant Professor, Department of Internal Medicine
Division of Cardiology Associate
Chief Medical Informatics Officer

Cardiologist Mujeeb Basit, M.D., M.M.Sc., is an expert in constructing and monitoring complex clinical decision support systems. Dr. Basit earned his undergraduate degree in computer science and worked at the Human Genome Project and the Dallas Heart Study prior to pursuing a medical degree. After completing cardiology fellowship, he earned a clinical informatics fellowship where he worked closely with national experts on clinical decision support and the use of machine learning to identify anomalous behaviors prior to them impacting clinical care. Dr. Basit joined the faculty at the University of Texas Southwestern Medical Center at Dallas in 2016, where he now serves as Assistant Professor of Internal Medicine and is Associate Chief Medical Informatics Officer. Dr. Basit has an interest in clinical process and outcomes improvement with the use of advanced minimally invasive decisions support. In addition to helping patients, Dr. Basit enjoys his role mentoring young physicians-in-training and teaching them the value of understanding informatics in day to day clinical use.

Available Credit

  • 1.00 AMA


Please login or create an account to take this course.

Required Hardware/software

Activities should be run with recent versions of common browsers, including Internet Explorer, Firefox and Google Chrome