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The application of machine learning models in a resource-constrained environment.

Addison M Heffernan1, Jaewook Shin1, Kemunto Otoki2

  • 1Division of Trauma and Surgical Critical Care, Department of Surgery, Brown University, Rhode Island Hospital, Providence, USA.

Irish Journal of Medical Science
|April 2, 2025
PubMed
Summary

Machine learning models effectively predict mortality in small intensive care unit datasets from resource-constrained settings. A tailored scoring system, Tropical Intensive Care Score (TropICS), performed comparably to established metrics in predicting patient outcomes.

Keywords:
Critical careICUMachine learning (ML)MortalityPredictive modelingResource-constrained settings

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Critical Care Medicine

Background:

  • Machine learning models (MLMs) typically require large datasets, posing challenges for resource-constrained intensive care units (ICUs).
  • Limited research exists on applying standard MLMs to small ICU datasets in low-resource settings.

Purpose of the Study:

  • To evaluate the efficacy of MLMs in predicting mortality using a small ICU dataset from a resource-constrained institution.
  • To assess the performance of a resource-constrained scoring system (TropICS) within MLMs.

Main Methods:

  • MLMs (XGBoost, KNN) were applied to a prospective cohort of mechanically ventilated patients in rural Kenya.
  • The Tropical Intensive Care Score (TropICS) was utilized as a key characteristic.
  • Area Under the Receiver Operating Characteristic curve (AUC) was calculated to predict mortality.

Main Results:

  • The study included 294 patients with a 60.2% mortality rate.
  • ML models demonstrated strong predictive performance for mortality (XGBoost AUC = 0.82).
  • The TropICS score showed performance comparable to APACHE-II and SAPS within MLMs.

Conclusions:

  • MLMs can be effectively implemented in small ICU datasets within resource-constrained environments.
  • ML models require validation before integration into clinical practice.
  • Contextualized scoring systems like TropICS perform well within MLMs.