Related Experiment Video
Updated: Jun 10, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Development of Machine Learning-Based Mpox Surveillance Models in a Learning Health System
Harry Reyes Nieva1,2, Jason Zucker1,3,4, Emma Tucker5
1Department of Biomedical Informatics, Columbia University, New York, NY, USA.
None:
We developed machine learning and deep learning models to identify mpox cases from clinical notes as part of a learning health system initiative. Lasso regression outperformed deep learning models, excelled in minimizing false positives, and may prove helpful for flagging missed or delayed diagnoses as part of continuous quality improvement.
Related Concept Videos
Steps in Outbreak Investigation
Models of Health Promotion and Illness Prevention I
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Principles of Disease Surveillance
Models of Health Promotion and Illness Prevention II
The agent-host-environment model states that disease results...

