Bibliographic Details
| Title: |
Visual assessment of interactions among resuscitation activity factors in out-of-hospital cardiopulmonary arrest using a machine learning model. |
| Authors: |
Kawai, Yasuyuki1 (AUTHOR) k6k6k@naramed-u.ac.jp, Okuda, Hirozumi1 (AUTHOR), Kinoshita, Arisa1 (AUTHOR), Yamamoto, Koji1 (AUTHOR), Miyazaki, Keita1 (AUTHOR), Takano, Keisuke1 (AUTHOR), Asai, Hideki1 (AUTHOR), Urisono, Yasuyuki1 (AUTHOR), Fukushima, Hidetada1 (AUTHOR) |
| Source: |
PLoS ONE. 9/6/2022, Vol. 17 Issue 9, p1-14. 14p. |
| Subject Terms: |
*CARDIAC arrest, *ARTIFICIAL neural networks, *RECEIVER operating characteristic curves, *MACHINE learning, *EMERGENCY medical services, *PROGNOSTIC models |
| Geographic Terms: |
JAPAN |
| Abstract: |
Aim: The evaluation of the effects of resuscitation activity factors on the outcome of out-of-hospital cardiopulmonary arrest (OHCA) requires consideration of the interactions among these factors. To improve OHCA success rates, this study assessed the prognostic interactions resulting from simultaneously modifying two prehospital factors using a trained machine learning model. Methods: We enrolled 8274 OHCA patients resuscitated by emergency medical services (EMS) in Nara prefecture, Japan, with a unified activity protocol between January 2010 and December 2018; patients younger than 18 and those with noncardiogenic cardiopulmonary arrest were excluded. Next, a three-layer neural network model was constructed to predict the cerebral performance category score of 1 or 2 at one month based on 24 features of prehospital EMS activity. Using this model, we evaluated the prognostic impact of continuously and simultaneously varying the transport time and the defibrillation or drug-administration time in the test data based on heatmaps. Results: The average class sensitivity of the prognostic model was more than 0.86, with a full area under the receiver operating characteristics curve of 0.94 (95% confidence interval of 0.92–0.96). By adjusting the two time factors simultaneously, a nonlinear interaction was obtained between the two adjustments, instead of a linear prediction of the outcome. Conclusion: Modifications to the parameters using a machine-learning-based prognostic model indicated an interaction among the prognostic factors. These findings could be used to evaluate which factors should be prioritized to reduce time in the trained region of machine learning in order to improve EMS activities. [ABSTRACT FROM AUTHOR] |
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| Database: |
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