@inproceedings{bbaf910e12a844cdbdf878f5ed110216,
title = "Enhancing medical education with data-driven software: The TrainCoMorb app",
abstract = "Medical education can take advantage of big data to enhance the learning experience of students. This paper describes the development of TrainCoMorb, an online, data-driven application for medical students who can practice recognizing comorbidities and their attributable risk for negative outcomes. Trainees access TrainCoMorb to create scenarios of comorbidities, step-by-step, and see snapshots of the risk for inpatient death, hospital septicemia and the projected length of stay. The study utilized an enormous claims dataset (N=11m.). A dynamic Bayesian algorithm was developed, which calculates and updates conditional probabilities for the outcomes under study in each phase of an ongoing scenario. The trainee initiates a scenario by selecting demographics and a principal diagnosis, then adds chronic and hospital-acquired conditions to see a summary of the attributable risk in each phase. TrainCoMorb is anticipated to assist medical students gain a better understanding of comorbidities and their impact on clinical outcomes.",
keywords = "Bayesian methods, Comorbidities, Medical education",
author = "Dimitrios Zikos and Neli Ragina and Oliver Strong",
note = "Funding Information: This research is supported by the Faculty Research and Creative Endeavors (FRCE), grant number: 48530 (01/2019, $8,000), PI: Dimitrios Zikos, Grant Title: Training Platform for Computer Assisted Construction and Risk Stratification of Comorbidity Profiles. Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.",
year = "2020",
doi = "10.3233/SHTI200499",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "83--86",
editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias",
booktitle = "THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC",
}