Development of a Machine Learning Model to Predict Cardiac Arrest during Transport of Trauma Patients.
cardiac arrest
emergency medical services
machine learning model
trauma
Journal
Journal of Nippon Medical School = Nippon Ika Daigaku zasshi
ISSN: 1347-3409
Titre abrégé: J Nippon Med Sch
Pays: Japan
ID NLM: 100935589
Informations de publication
Date de publication:
30 May 2023
30 May 2023
Historique:
medline:
2
6
2023
pubmed:
24
2
2023
entrez:
23
2
2023
Statut:
ppublish
Résumé
Trauma is a serious medical and economic burden worldwide, and patients with traumatic injuries have a poor survival rate after cardiac arrest. The authors developed a prediction model specific to prehospital trauma care and used machine learning techniques to increase its accuracy. This retrospective observational study analyzed data from patients with blunt trauma injuries due to traffic accidents and falls from January 1, 2018, to December 31, 2019. The data were collected from the National Emergency Medical Services Information System, which stores emergency medical service activity records nationwide in the United States. A random forest algorithm was used to develop a machine learning model. The prediction model had an area under the curve of 0.95 and a negative predictive value of 0.99. The feature importance of the predictive model was highest for the AVPU (Alert, Verbal, Pain, Unresponsive) scale, followed by oxygen saturation (SpO The machine learning model was highly accurate in identifying patients who did not develop cardiac arrest.
Sections du résumé
BACKGROUND
BACKGROUND
Trauma is a serious medical and economic burden worldwide, and patients with traumatic injuries have a poor survival rate after cardiac arrest. The authors developed a prediction model specific to prehospital trauma care and used machine learning techniques to increase its accuracy.
METHODS
METHODS
This retrospective observational study analyzed data from patients with blunt trauma injuries due to traffic accidents and falls from January 1, 2018, to December 31, 2019. The data were collected from the National Emergency Medical Services Information System, which stores emergency medical service activity records nationwide in the United States. A random forest algorithm was used to develop a machine learning model.
RESULTS
RESULTS
The prediction model had an area under the curve of 0.95 and a negative predictive value of 0.99. The feature importance of the predictive model was highest for the AVPU (Alert, Verbal, Pain, Unresponsive) scale, followed by oxygen saturation (SpO
CONCLUSIONS
CONCLUSIONS
The machine learning model was highly accurate in identifying patients who did not develop cardiac arrest.
Identifiants
pubmed: 36823128
doi: 10.1272/jnms.JNMS.2023_90-206
doi:
Types de publication
Observational Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM