Technical Note
Version 1
Preserved in Portico This version is not peer-reviewed
Beyond Traditional Covariates in Medical Informatics
Version 1
: Received: 5 November 2019 / Approved: 7 November 2019 / Online: 7 November 2019 (09:25:04 CET)
How to cite: Kartoun, U. Beyond Traditional Covariates in Medical Informatics. Preprints 2019, 2019110073. https://doi.org/10.20944/preprints201911.0073.v1 Kartoun, U. Beyond Traditional Covariates in Medical Informatics. Preprints 2019, 2019110073. https://doi.org/10.20944/preprints201911.0073.v1
Abstract
Deep behavioral covariates (DBCs) introduced in this perspective form a new class of covariates that have the potential to enhance the performance of predictive models and improve analytics in clinical decision support applications. DBCs can measure how engaged a patient tends to be and how he or she tends to respond to events, and they may be highly predictive of the patient’s outcomes for a planned treatment. DBCs may potentially serve as a standard to measure patient engagement and activation and may form highly efficient mechanisms for improving patient outcomes.
Keywords
deep behavioral covariates; clinical informatics; predictive modeling; electronic medical records; machine-learning; data-mining
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment