TY - GEN
T1 - Trauma care decision support under fire
AU - Nemeth, Christopher
AU - Pickering, Brian
AU - Amos-Binks, Adam
AU - Harrison, Andrew
AU - Pinevich, Yuliya
AU - Lowe, Ryan
AU - Rule, Gregory
AU - Laufersweiler, Dawn
AU - Herasevich, Vitaly
N1 - Funding Information:
This work is supported by the US Army Medical Research and Materiel Command under Contract No. ! 0001.The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of the Army position, policy or decision unless so designated by other documentation. In the conduct of research where humans are the subjects, the investigator(s) adhered to the policies regarding the protection of human subjects as prescribed by Code of Federal Regulations (CFR) Title 45, Volume 1, Part 46; Title 32, Chapter 1, Part 219; and Title 21, Chapter 1, Part 50 (Protection of Human Subjects).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Combat medics care for battlefield casualties at point of injury (PoI), and stabilize and transport them to field care facilities. Casualty care requires complex decisions under austere and frequently dangerous conditions. Future operations are not expected to have immediate evacuation available, requiring more complex care and potential for complications including circulatory shock, which is a life-threatening state that can lead to organ failure and death if not detected and treated. We report on a project to develop decision support for combat medics (Role 1) and Battalion Aid Station/Field Hospital clinicians (Role 2) by combining resources of two proven intensive care unit (ICU) decision support systems. Machine learning algorithms are under development that will be used to detect the probability of shock in a casualty. Algorithms will be compared with actual clinical decisions in a 'silent test.' A parallel effort is developing a software program for use with a vital signs sensor that can be installed on a DoD-approved version of the Samsung smart phone and tablet, then evaluated in the field by medics and clinicians. Faster, more accurate decisions can improve care for trauma patients, in circumstances where delays can increase morbidity and mortality.
AB - Combat medics care for battlefield casualties at point of injury (PoI), and stabilize and transport them to field care facilities. Casualty care requires complex decisions under austere and frequently dangerous conditions. Future operations are not expected to have immediate evacuation available, requiring more complex care and potential for complications including circulatory shock, which is a life-threatening state that can lead to organ failure and death if not detected and treated. We report on a project to develop decision support for combat medics (Role 1) and Battalion Aid Station/Field Hospital clinicians (Role 2) by combining resources of two proven intensive care unit (ICU) decision support systems. Machine learning algorithms are under development that will be used to detect the probability of shock in a casualty. Algorithms will be compared with actual clinical decisions in a 'silent test.' A parallel effort is developing a software program for use with a vital signs sensor that can be installed on a DoD-approved version of the Samsung smart phone and tablet, then evaluated in the field by medics and clinicians. Faster, more accurate decisions can improve care for trauma patients, in circumstances where delays can increase morbidity and mortality.
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U2 - 10.1109/SMC.2019.8914242
DO - 10.1109/SMC.2019.8914242
M3 - Conference contribution
AN - SCOPUS:85076795978
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3705
EP - 3709
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -