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Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock.

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  • 1Applied Research Associates, Albuquerque, NM 87110, USA.

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A new mobile decision support system uses machine learning to help medics detect and differentiate shock in prehospital tactical combat casualty care, predicting onset 90 minutes in advance with over 75% accuracy.

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Prehospital Care

Background:

  • Complex Prolonged Field Care in austere settings requires enhanced support for inexperienced providers.
  • Effective tools are needed to assist in treating patients during tactical combat casualty care.
  • Accurate detection and differentiation of shock are critical in prehospital settings.

Purpose of the Study:

  • To develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care.
  • To utilize machine learning for detecting and differentiating shock manifestations.
  • To augment medic decision-making with salient patient data.

Main Methods:

  • Software interface developed through literature review, rapid prototyping, and expert design reviews.
  • Machine learning model trained on MIMIC and de-identified Mayo Clinic ICU data.
  • Logistic regression model selected as the best performing algorithm.

Main Results:

  • 17 military medical subject matter experts provided design requirements.
  • The machine learning algorithm predicted shock onset 90 minutes prior to occurrence.
  • The model achieved over 75% accuracy in predicting shock in the test dataset.

Conclusions:

  • The Trauma Triage, Treatment, and Training Decision Support system will enhance medic decision-making.
  • The system aids in diagnosing multiple types of shock.
  • Remotely trained, field-deployed machine learning models support enhanced care.