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Machine learning in cardiac surgery: a narrative review.

Travis J Miles1,2, Ravi K Ghanta1,2

  • 1Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA.

Journal of Thoracic Disease
|May 13, 2024
PubMed
Summary

Machine learning (ML) shows promise in cardiac surgery for predicting outcomes, but its clinical utility is still limited. Integrating ML with electronic health records could enhance decision support and improve patient care.

Keywords:
Cardiac surgeryartificial intelligence (AI)critical caredata sciencemachine learning (ML)

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Area of Science:

  • Cardiovascular Surgery
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Machine learning (ML) is increasingly applied to medical challenges.
  • In cardiac surgery, ML is used for risk stratification and outcome prediction.
  • The clinical utility of ML in this field requires further clarification.

Purpose of the Study:

  • To review ML applications in cardiac surgery.
  • To assess ML's utility in predictive analytics.
  • To explore ML's implications for clinical decision support.

Main Methods:

  • Narrative review of PubMed-indexed articles since 2000.
  • Search terms included ML, supervised ML, deep learning, AI, cardiovascular surgery, and thoracic surgery.

Main Results:

  • ML accurately predicts clinical outcomes in cardiac surgery, particularly for pre-operative risk profiles.
  • Improvements over traditional risk metrics are modest.
  • Current clinical applications of ML remain limited.

Conclusions:

  • ML excels with high-volume, multidimensional data like EHRs.
  • Models trained on ICU data show strong predictive performance.
  • Integrating ML into EHRs can create dynamic decision support for improved cardiac surgery outcomes.