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Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study
- Source :
- BMC Medical Informatics and Decision Making, Vol 23, Iss 1, Pp 1-9 (2023)
- Publication Year :
- 2023
- Publisher :
- BMC, 2023.
-
Abstract
- Abstract Background This study aimed to develop a prediction model for transferring patients to an inappropriate hospital for suspected cardiovascular emergency diseases at the pre-hospital stage, using variables obtained from an integrated nationwide dataset, and to assess the performance of this model. Methods We integrated three nationwide datasets and developed a two-step prediction model utilizing a machine learning algorithm. Ninety-eight clinical characteristics of patients identified at the pre-hospital stage and 13 hospital components were used as input data for the model. The primary endpoint of the model was the prediction of transfer to an inappropriate hospital. Results A total of 94,256 transferred patients in the public pre-hospital care system matched the National Emergency Department Information System data of patients with a pre-hospital cardiovascular registry created in South Korea between July 2017 and December 2018. Of these, 1,770 (6.26%) patients failed to be transferred to a capable hospital. The area under the receiver operating characteristic curve of the final predictive model was 0.813 (0.800–0.825), and the area under the receiver precision-recall curve was 0.286 (0.265–0.308). Conclusions Our prediction model used machine learning to show favorable performance in transferring patients with suspected cardiovascular disease to a capable hospital. For our results to lead to changes in the pre-hospital care system, a digital platform for sharing real-time information should be developed.
Details
- Language :
- English
- ISSN :
- 14726947
- Volume :
- 23
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Medical Informatics and Decision Making
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7b9ed529d6b94321b29f9eccc65dd260
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12911-023-02149-9