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Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With

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Summary

This study developed an explainable deep learning model for predicting in-hospital mortality in acute myocardial infarction (AMI) patients. The model identified older age, high heart rate, and low body temperature as key mortality predictors.

Keywords:
acute myocardial infarctiondeep learningexplainable modelmortalityprediction

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Acute myocardial infarction (AMI) presents a high risk of in-hospital mortality.
  • Current deep learning models for AMI mortality prediction lack interpretability.

Purpose of the Study:

  • To establish an explainable deep learning model for individualized in-hospital mortality prediction in AMI patients.
  • To assess key risk factors contributing to mortality.

Main Methods:

  • Retrospective multicenter study using AMI patient data (July 2016-December 2022).
  • Data split into training, internal test, and external validation sets.
  • Compared deep learning models with linear and tree-based models, using Shapley Additive Explanations for interpretability.

Main Results:

  • A Self-Attention and Intersample Attention Transformer model achieved the highest predictive performance (AUC 0.86 internal, 0.85 external).
  • Key predictors for increased mortality included older age, high heart rate, and low body temperature.
  • Model demonstrated strong performance across internal and external validation cohorts.

Conclusions:

  • An explainable deep learning model can effectively predict mortality in AMI patients.
  • Identified older age, unstable vital signs, and metabolic disorders as significant risk factors for mortality.