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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...
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Related Experiment Video

Updated: May 15, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Published on: May 15, 2020

Learning to predict post-hospitalization VTE risk from EHR data.

Emily Kawaler1, Alexander Cobian, Peggy Peissig

  • 1University of Wisconsin-Madison, Madison, WI, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 11, 2013
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts post-hospitalization venothromboembolism (VTE) risk using electronic health records (EHR). This approach identifies new risk factors and surpasses existing scoring models for VTE prediction.

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Last Updated: May 15, 2026

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Published on: May 15, 2020

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Clinical Risk Prediction

Background:

  • Venothromboembolism (VTE) is a significant post-hospitalization risk.
  • Accurate prediction of VTE risk is crucial for patient management.
  • Existing VTE scoring models have limitations.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting post-hospitalization VTE risk.
  • To identify novel risk factors for VTE using EHR data.
  • To compare the performance of machine learning models against existing VTE scoring systems.

Main Methods:

  • Utilized machine learning algorithms to analyze electronic health record (EHR) data.
  • Employed a case-control study design for model training and validation.
  • Compared predictive accuracy of induced models with established VTE scoring models.

Main Results:

  • Identified several previously unrecognized risk factors for VTE.
  • Machine learning models demonstrated superior accuracy in identifying high-risk patients compared to existing scoring models.
  • Accurate VTE prediction models were learned without prior knowledge of specific risk factors.

Conclusions:

  • Machine learning offers a powerful tool for predicting post-hospitalization VTE risk.
  • EHR data can be effectively leveraged to discover novel VTE risk factors.
  • ML-based prediction models represent an advancement over current VTE risk assessment strategies.