考量主訴之急診住院預測研究：BERT模型開發
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2022
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近來大醫院急診持續嚴重壅塞，日益增加的病患人數造成急診醫療資源供不應求，長期以來急診壅塞的問題也導致延誤病患的就診或住院的時間。本研究以合作醫院「台北馬偕紀念醫院」之2011年至2018年之八年度急診室病歷資料，合計共1,065,480筆急診病患於檢傷階段之就醫紀錄，以預測住院之可能性。研究首先採用自然語言處理之BERT預訓練模型進行微調訓練，透過急診室病歷資料之主訴語進行住院預測。研究結果發現經過不平衡處理的BERT模型，期住院預測結果之AUC指標可達0.950、Accuracy指標可達0.891；此外，透過特定檢傷資料（檢傷一級與檢傷五級）進行預測結果AUC指標可達0.954、Accuracy指標可達0.960。然而單獨只考慮結構化變數，如：檢傷等級、年齡、體溫、到院時間與到院方式，並採用弱分類器XGBoost模型之預測效力，其不如以主訴透過BERT模型之預測結果。故研究進一步比較XGBoost透過篩選之病患五項結構化重要特徵所生成之擴充主訴，納入BERT模型預測之效力，透過BERT訓練後，其AUC指標可達0.958、Accuracy指標可達0.904，遠高於過去的相關研究。研究方法與發現提供急診住院預測參考，並期盼降低急診室病床等候時間，進而改善急診壅塞問題。
In recent years, emergency departments (EDs) of hospitals are crowded constantly. The issue of EDs congestion is due to the prolonged situation of public demands exceeding the supply of EDs medical resources, and the increasing numbers of patients. As the result, this issue may delay a patient’s time for receiving treatments or patient admissions. To predict the probability of patient admissions, the study was based on 2011-2018 EDs medical records of the collaborated hospital MacKay Memorial Hospital in Taipei (Taiwan). This 8-year dataset included a total of 1,065,480 triage medical records of EDs.The study adopted Bidirectional Encoder Representations from Transformers(BERT), a pre-training model in natural language processing(NLP) for fine-tuning. By training the chief complaints (CCs) on EDs medical records, the study aimed to predict the possibility of patient admissions. The result carried out by a BERT model that underwent imbalanced processing was that the AUC index reached 0.950, and the Accuracy index reached 0.891 for the possibility of patient admissions. On top of that, by analyzing specific levels of triage (level 1st and level 5th), the prediction result reached 0.954 on AUC index and 0.960 on Accuracy index. Nevertheless, when only considering structured variables, for instance, the levels of triage, age, body temperature, arrival time, and mode of arrival, and adopting weak classifier eXtreme Gradient Boosting (XGBoost). The predictive validity was lower than the result of CC data undergone BERT. Therefore, the study took one step forward. First, this study further screened the five important variables by comparing with the structured characteristics of the EDs medical records through XGBoost. Later, inserted the variables into the CC-expanded of BERT. And after BERT Training, the AUC index reached 0.958 and the Accuracy index reached 0.904, which was far higher than the related studies in the past. The research methods and findings are for references to predict EDs presentations and hospital admissions. Furthermore, the study aimed to decrease the waiting time for EDs beds and reduce EDs congestion.
In recent years, emergency departments (EDs) of hospitals are crowded constantly. The issue of EDs congestion is due to the prolonged situation of public demands exceeding the supply of EDs medical resources, and the increasing numbers of patients. As the result, this issue may delay a patient’s time for receiving treatments or patient admissions. To predict the probability of patient admissions, the study was based on 2011-2018 EDs medical records of the collaborated hospital MacKay Memorial Hospital in Taipei (Taiwan). This 8-year dataset included a total of 1,065,480 triage medical records of EDs.The study adopted Bidirectional Encoder Representations from Transformers(BERT), a pre-training model in natural language processing(NLP) for fine-tuning. By training the chief complaints (CCs) on EDs medical records, the study aimed to predict the possibility of patient admissions. The result carried out by a BERT model that underwent imbalanced processing was that the AUC index reached 0.950, and the Accuracy index reached 0.891 for the possibility of patient admissions. On top of that, by analyzing specific levels of triage (level 1st and level 5th), the prediction result reached 0.954 on AUC index and 0.960 on Accuracy index. Nevertheless, when only considering structured variables, for instance, the levels of triage, age, body temperature, arrival time, and mode of arrival, and adopting weak classifier eXtreme Gradient Boosting (XGBoost). The predictive validity was lower than the result of CC data undergone BERT. Therefore, the study took one step forward. First, this study further screened the five important variables by comparing with the structured characteristics of the EDs medical records through XGBoost. Later, inserted the variables into the CC-expanded of BERT. And after BERT Training, the AUC index reached 0.958 and the Accuracy index reached 0.904, which was far higher than the related studies in the past. The research methods and findings are for references to predict EDs presentations and hospital admissions. Furthermore, the study aimed to decrease the waiting time for EDs beds and reduce EDs congestion.
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急診室, 住院預測, 主訴, BERT, XGBoost, Emergency Departments (EDs), Prediction of Hospital Admission, Chief Complaints (CCs), BERT, XGBoost