Research and practice on intelligent classification of medical safety incidents based on deep BERT

Corresponding author: PENG Hua, pengh@pumch.cn

DOI: 10.12201/bmr.202312.00021

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  Abstract: Purpose/Significance To improve the manual mode of medical safety event classification and evaluation, and improve work efficiency and timeliness. Method/Process Select past medical safety event data for preprocessing, use BERT (Bidirectional Encoder Representations from Transformers)model for training, testing, and iterative optimization, and construct an intelligent classification and prediction model for medical safety events. Result/Conclusion The model was used to classify 466 medical safety incidents reported by clinical departments from January to November 2022, including 267 medical adverse events (including 25 Level I events, 105 Level II events, 36 Level III events, and 101 Level IV events) and 199 patient related safety hazards, F1 value reaches 0.66. Applying BERT to assist in the classification and evaluation of medical safety incidents can improve the efficiency and timeliness of this work to a certain extent, and help to intervene in medical safety risk hazards in a timely manner.

  Key words: Medical safety incidents; BERT; Deep learning; Intelligent classification

  Submit time: 14 December 2023

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2023-07-12	bmr.202312.00021V1	Download

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