Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock.
Introduction: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system fo...
| Publicado en: | Military Medicine Vol. 186; pp. 273 - 281 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
2021 Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148599340&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148599340 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00264075 4DV jtl: Military Medicine issn: 00264075 maglogo: N pubinfo: dt: 2021 Supplement vid: 186 pid: 622 pub: Oxford University Press / USA artinfo: ui: 148599340 148599340 NLM33499479 148599340 10.1093/milmed/usaa275 NLM33499479 148599340 ppf: 273 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock. aug: au: Nemeth, Christopher Amos-Binks, Adam Burris, Christie Keeney, Natalie Pinevich, Yuliya Pickering, Brian W. Rule, Gregory Laufersweiler, Dawn Herasevich, Vitaly Sun, Mei G. affil: Applied Research Associates, Albuquerque, NM 87110, USA. sug: subj: Military Personnel Military Medicine Shock Triage Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Introduction: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation.Materials and Methods: Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews. Machine learning algorithm methods included development of a model trained on publicly available Medical Information Mart for Intensive Care data, then on de-identified data from Mayo Clinic Intensive Care Unit.Results: The project team interviewed 17 Army, Air Force, and Navy medical subject matter experts during design requirements review sessions. They had an average of 17 years of service in military medicine and an average of 4 deployments apiece and all had performed tactical combat casualty care on live patients during deployment. Comments provided requirements for shock identification and management in prehospital settings, including support for indication of shock probability and shock differentiation. The machine learning algorithm based on logistic regression performed best among other algorithms we tested and was able to predict shock onset 90 minutes before it occurred with better than 75% accuracy in the test dataset.Conclusions: We expect the Trauma Triage, Treatment, and Training Decision Support system will augment a medic's ability to make informed decisions based on salient patient data and to diagnose multiple types of shock through remotely trained, field deployed ML models. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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