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Updated: Aug 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Building an automated, machine learning-enabled platform for predicting post-operative complications.

Jeremy A Balch1,2, Matthew M Ruppert1,3, Benjamin Shickel1,3

  • 1Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America.

Physiological Measurement
|January 19, 2023
PubMed
Summary
This summary is machine-generated.

The MySurgeryRisk algorithm predicts post-operative complications using electronic health record data. This model enhances patient assessment and treatment by improving predictive analytics for surgical outcomes.

Keywords:
artificial intelligenceclinical decision supportmachine learningpost operative complicationssurgery

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Area of Science:

  • Medical Informatics
  • Clinical Decision Support
  • Predictive Analytics in Healthcare

Background:

  • The University of Florida College of Medicine developed the MySurgeryRisk algorithm in 2019.
  • The algorithm leverages automatically extracted data from electronic health records.
  • It aims to predict eight major post-operative complications.

Purpose of the Study:

  • To detail the construction and improvement of the MySurgeryRisk predictive model.
  • To showcase the integration of data processing and predictive analytics.
  • To discuss the expansion and future directions of the algorithm.

Main Methods:

  • Consolidation of a comprehensive database.
  • Processing of both fixed and time-series physiological measurements.
  • Development and training of predictive models for patient outcomes.

Main Results:

  • The MySurgeryRisk algorithm efficiently processes EHR data for complication prediction.
  • The model has been refined and expanded for broader patient assessment.
  • The study highlights advancements in data processing for clinical decision support.

Conclusions:

  • The MySurgeryRisk algorithm represents a significant advancement in predicting post-operative complications.
  • The model's development showcases the power of integrated data analytics in medicine.
  • Future work will focus on further enhancing the algorithm's predictive capabilities and clinical utility.