６.1 結論
本研究針對文字檢驗報告自動結構化的問題，提出先以字詞詞性規則擷取出 醫學詞彙候選詞，建立檢驗報告專屬的醫學字典，並經過字與字出現的關聯運算，
篩除字典中可能的錯字或無特殊語意的字詞。針對特殊檢驗項目段落之檢驗細項 關鍵字詞擷取方法，本研究先將前述建立的字典進行去除雜訊字詞的處理，再利 用 LDA 主題機率模型分析，擷取出檢驗項目細項之候選關鍵字詞。而對摘要式檢 驗報告結構化的方法，則透過將段落報告與醫學字典進行字詞的比對擷取及前後 詞彙合併，並將擷取出的字詞與檢驗摘要項目關鍵字尾比對，有效將其分配到所 屬摘要項目中。論文並將上述提出的方法以系列實驗評估成效，實驗結果可顯示 本研究提出方法的有效性。
６.2 未來方向
由實驗結果觀察到在進行摘要式段落結構化的處理過程中，少部分的重要字 詞會有遺漏的狀況，無法用目前提出的字詞詞性規則擷取出。未來的研究可進一 步考慮從大量檢驗報告資料庫，以及已知的醫學詞彙，自動學習出字詞樣式規則，
以提供更完整有效的結構化擷取。另一方面，未來可考慮進一步如何將找出的檢 驗項目細項之關鍵字詞，分析出其分項架構關係。
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附錄一
刪除之結尾字
finding comment correlate microscopy presentation
course correlation history examination possibility
consideration survey result myeloma sclerosis
cause patient smoking treatement due
drug data condition according setting
age feature background information study
profile year criterion criteria exmaination
likehood micropsy exmaintation usage limitation
response diagnosis evidence pending conclusion
impression evaluation clarification em show ultrastructure show
red staining pattern
刪除之字首
including mostly only favoring otherwise
involving