Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.
Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparam...
| Published in: | Medical & Biological Engineering & Computing Vol. 52; no. 2; pp. 193 - 204 |
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| Main Authors: | , , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Feb2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104010954&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104010954 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2014 vid: 52 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104010954 NLM24263362 2012455924 10.1007/s11517-013-1130-x NLM24263362 104010954 ppf: 193 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients. aug: au: Liu, Nehemiah T Holcomb, John B Wade, Charles E Batchinsky, Andriy I Cancio, Leopoldo C Darrah, Mark I Salinas, José affil: US Army Institute of Surgical Research, 3650 Chambers Pass, Building 3610, Fort Sam Houston, TX 78234-6315, USA, nehemiah.liu@us.army.mil. sug: subj: Artificial Intelligence Models, Theoretical Wounds and Injuries Diagnosis Adolescence Adult Aged Aged, 80 and Over Algorithms Resource Databases Female Heart Rate Physiology Human Logistic Regression Male Middle Age Neural Networks (Computer) Prospective Studies Reproducibility of Results Respiratory Rate Physiology Retrospective Design Wounds and Injuries Therapy Young Adult Adolescent: 13-18 years Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82 h of trauma patient data showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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