Technical Name 智慧醫院 ICD10 病歷分類自動編碼系統
Project Operator Graduate Institute of Biomedical ElectronicsBioinformatics, National Taiwan University
Project Host 賴飛羆
Summary Use NLP techniques to realize the automatic coding of ICD10. According to the input of the patient’s age, gender, medical order, admissions, progress note, surgical records, discharge, ICD-10 diagnostic codeICD-10 disposal code, perform machine learning model trainingcode prediction. In addition, the combination codemedical order-related coding rules in practice are used to establish corresponding rules to optimize the accuracy of AI prediction.
Technical Film
Scientific Breakthrough The coders classify disease according to the medical records. Each group was randomly assigned the medical records,we provided the ICD code predicted by the best DNN classification model. We compared the elapsed timeF1 scores,then analyzed them with paired samples. The results showed that the ICD codes that provide predictions can increase the average F1 of coders from the median from 0.832 to 0.922 (P 0.05).
Industrial Applicability At the part of the health insurance declaration, it is accurately classified as the drop point of the correct DRGobtains the medical income that the hospital deserves. This allows doctors to predict the DRG early when the patient is hospitalized,to reduce the burden of medicalhealth insurance. On the patient side, the pre-coded mechanism can be used by the insurance company as the underwriting basis when the patient is hospitalized, speeding up the hospitalization underwriting payment mechanism,facilitating the payment of the patient.
Matching Needs 天使投資人、策略合作夥伴
Keyword NLP Bert ICD-10 Machine learning Neural network Text classification
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