Related Experiment Video
Updated: Jul 9, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Use of a machine learning model to predict retention in care in an urban HIV clinic
Sarah A Schmalzle1, Demetri Maroosis2, Henry Masur3
1Institute of Human Virology, University of Maryland School of Medicine, Baltimore.
Abstract:
Identifying barriers to retention in care (RIC) is critical to ending the HIV epidemic in the United States. Therefore, we developed a machine learning model (MLM) to identify predictive factors for RIC in an urban HIV clinic. Our MLM yielded a positive predictive value of 84%, higher than previously reported MLMs. We found that MLM can be used to develop interventional strategies to enhance RIC in HIV care.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Related Concept Videos
Kaplan-Meier Approach
Steps in Outbreak Investigation
Retrovirus Life Cycles