Construction and Influence of Auxiliary Analysis Model for Inpatient Medical Record Front Page
Objectives This study aims to construct an artificial intelligence preliminary screening model for the front page of inpatient medical records and apply it to analysis,so as to provide a reference for improving the quality of the front page of inpatient medical records.Methods A total of 5000 discharged medical records from a hospital from January 1,2020 to August 31,2021 were randomly selected as the object of model construction.The front pages of all inpatient medical records were divided into a training set(n=3500)and a test set(n=1500)according to a ratio of 7:3.The BERt-iterative expansive Convolutional neural network(IDCNN)-multi-head attention mechanism(MHA)-stochastic conditional field(CRF)was constructed based on the bidirectional rich semantic pre-trained language model(BERT)mechanism to construct the auxiliary analysis model of the front page of inpatient medical records and analyzed the front page of two sets of inpatient medical records.At the same time,the quality control doctor in the medical records room conducted primary quality control on the front page of the inpatient medical records in both the training set and the test set.Subsequently,the quality control information on the front page of the inpatient medical records was reviewed by the attending physician with more than 5 years of work experience,and the manual quality control results were recorded.Finally,the consistency test between the model and manual audit results was carried out to verify the audit effect of BIMC model.Results The coincidence rates of BIMC model and manual evaluation results in training set and validation set were 93.00％(3255/3500)and 90.73％(1361/1500).The R-CNN model in the two sets had a high consistency with the human evaluation results[Kappa=0.921(training)/0.915(validation),both P＜0.001].Conclusions The constructed BIMC model has a high consistency with manual analysis in the analysis of the front page of inpatient medical records,and it is feasible to apply it to the analysis of the front page of inpatient medical records.However,it is still necessary to further incorporate more inpatient medical records to improve the accuracy of the model in identifying error information.
Artificial intelligence modelBidirectional encoder representations from transformersFront page of medical recordsDiagnosis related groups
万方数据
维普
