隨著醫學資訊的進展，以及電子病歷的普及，如何準確且快速地評估醫療品質，是醫學界一項重要的課題。本研究欲藉由整合NLP 以及文件探勘等技術，建立一個自動化的醫療品質評估系統，透過探勘醫療品質的過程面以及結果面，作進一步地評估。本研究將急性心肌梗塞作為指標性疾病。研究資料為台大醫院的急性心肌梗塞病患的電子出院病摘。將所有罹患急性心肌梗塞的病人，區分成兩大
類別STEMI, NSTEMI 作為訓練樣本來建構系統，以及測試樣本來驗證系統。能夠獲得相當高的一致性。最後實際應用於該醫院的心臟外科部門，作為評估該部門的醫療品質。我們藉由評估醫療品質可以清楚地對該治療單位的醫療照顧，作全面性的探討。進而減少醫療失誤，以促進病人安全。

With the progress of medical informatics and popularity of electronic patient records, how to evaluate the quality of care accurately and rapidly is an important issue in medical domain recently. In this study, we propose to integrate natural language processing and text mining techniques to develop an automatic system to assess the
quality of care for acute myocardial infarction (AMI). We make a thorough analys is by evaluating process part and outcome part of quality of care. We collect the Electronic
Discharge Notes (EDN) and some other related tables in NTU hospital in Taiwan as dataset. It separates the patients’ record for AMI into two classes, STEMI and NSTEMI, as training set and testing set in our system. And we get the high consistence when building and validating. Finally, we apply the system on the cardiac surgical department and assess the quality of care. We can monitor the quality of the treatment unit derived from assessing quality of care and reduce medical errors, elevate patient safety further.

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