Supervised Machine Learning


 

This presentation will focus on supervised machine learning and automated machine learning within the healthcare disciplines. The presenter will show studies which demonstrate how such platforms guide translational studies in various clinical context.

Originally presented on November 19, 2020 in Salt Lake City, Utah.


Lecture Presenter

Hooman H. Rashidi, MD, MS, FASCP

Hooman H. Rashidi, MD, MS, FASCP

Professor & Vice Chair of GMEVice Chair of Informatics & Computational PathologyDirector of Residency ProgramDirector of Flow Cytometry & Immunology,
Department of Pathology and Laboratory Medicine, University of California, Davis School of Medicine

Dr. Rashidi’s experience in bioinformatics dates to his graduate years at UCSD, which subsequently allowed him to serve as the principal author and editor of several popular bioinformatics textbooks. This background also enabled him to develop various novel Artificial Intelligence (AI)/Machine learning (ML) platforms and has led to active and exciting research in clinical, educational, and quality improvement projects, which now includes over 30 collaborators from multiple departments and various prominent institutions. These studies have also led to numerous manuscripts published in leading journals. Additionally, Dr. Rashidi’s efforts have led to several filed patents within the machine learning arena, including one on early identification of Acute Kidney Injury, another on early sepsis prediction, and his most recent filed patent, which is based on his unique proprietary Automated Machine Learning (Auto-ML) software known as MILO (Machine Intelligence Learning Optimizer). MILO now serves as a powerful and validated new Auto-ML tool for many clinical, quality, and educational projects and was just licensed to an industry partner by the University of California. Dr. Rashidi’s efforts are widely recognized nationally and internationally, as evidenced by his various invitations to speak at prestigious conferences and institutions.


Objectives

After this presentation, participants will be able to:

  • Review basic concepts of machine learning
  • Define supervised machine learning

Sponsored by:

University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories