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Related Experiment Video

Updated: Mar 15, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Development of a Machine Learning-Based Predictive Model and Clinically Oriented Web Application for 30-Day Mortality

Telmo Miguel-Medina1, Susel Góngora Alonso1, Isabel de la Torre Díez1

  • 1eHealth and Telemedicine Group (GTe), University of Valladolid, 47011 Valladolid, Spain.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

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This summary is machine-generated.

A new machine learning model accurately predicts 30-day mortality in cardiac surgery patients. A web application allows real-time risk assessment, aiding clinical decision-making.

Area of Science:

  • Cardiovascular Surgery
  • Medical Informatics
  • Machine Learning

Background:

  • Cardiac surgery carries significant 30-day mortality risks.
  • Accurate preoperative risk assessment is crucial for patient management.
  • Existing prediction models may lack real-time clinical integration.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting 30-day mortality in cardiac surgery.
  • To create a clinician-oriented web application for real-time model implementation.
  • To enhance preoperative risk assessment and clinical decision support.

Main Methods:

  • Retrospective analysis of 325 cardiac surgery patients.
  • Supervised machine learning, including XGBoost model training and cross-validation.
Keywords:
XGBoostcardiac surgeryclinical decision supportexplainabilitymachine learningmortality predictionweb application

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Last Updated: Mar 15, 2026

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  • Development of a StreamLit-based web application with SHAP explainability.

    • Main Results:

    • XGBoost model achieved high performance: AUC-ROC of 0.968, recall of 0.800, Brier score of 0.058.
    • Web application provides real-time mortality predictions with model transparency.
    • Clinician feedback indicated the tool is intuitive and valuable for risk assessment.

    Conclusions:

    • A robust ML model integrated with a functional web application offers a practical tool for cardiac surgery decision-making.
    • The combined approach improves accuracy and accessibility of risk prediction.
    • Further multicentre validation and user-centered refinement are planned.