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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/30953

Title: 醫療問診對話系統中省略現象之處理
Ellipsis Handling in A Medical Diagnosis Dialog System
Authors: 鮑建威
Contributors: NTOU:Department of Computer Science and Engineering
Keywords: 省略;醫療問診對話系統
Date: 2012
Issue Date: 2012-04-16T03:20:17Z
Abstract: 本論文提出了一個系統,在電腦化虛擬病人系統(computerized virtual patient, CVP)這個特定領域對話系統裡處理問診對話中出現的省略現象。 虛擬病人 (virtual patient) 是用來訓練醫學院學生推理判斷病情的重要教學方法。虛擬病人系統需要解決的問題就是問診對話中常常會出現口語化的現象,包含本論文的重點省略現象,若不處理省略,在教案的標準問題集中也不易比對到相對應的問題,找到相對應的答案。 省略現象的處理分為偵測省略、省略型態分類和省略還原。過去有許多特定領域下的對話系統,卻沒有類似問診對話領域的研究。我們根據被省略資訊,將省略分成幾大類。問診對話樣板儲存問診對話中我們所需的資訊。 本論文的系統為一個混合系統,規則式模組與機器學習,規則式模組利用問診對話樣板的資訊進行省略偵測,若無法由樣板內資訊偵測,由機器學習所訓練的省略偵測分類器偵測是否有省略出現,還原模組利用問診對話樣板儲存的資訊進行還原。我們針對問診對話是否有省略設計相關特徵,包含字面上資訊、斷詞結果、詞性、動詞時態、標點符號和觀察到的特殊詞彙等等,各特徵亦可搭配組合,找出最佳結果。最後再由規則式模組處理省略分類及還原。 實驗資料來自虛擬病人的實際教學錄音和醫院中實際的門診對話錄音,機器學習方法為CRF (Condition Random Field) ,測試時採10等份交叉驗證法 (10-fold- cross-validation)。 實驗訓練出的最佳系統,以機器學習方式偵測省略的f-value為86.73%,與還原模組合併f-value為78.95%,而利用樣板填入情形偵測省略效能為82.58%而還原效能為81.07%,整體效能f-value為85.54%。面對測試資料集時,整體效能precision為79.37%,recall為77.4%,f-value為78.35%。
Computerized virtual patient (CVP) is a domain specific dialog system. In this system, we handle ellipsis in medical diagnosis. Virtual patient is an important teaching method for medical college’s student. It can help student to learn how to judge patient’s condition from medical diagnosis. CVP need to resolve oral phenomenon something like our goal ellipsis. If we don’t handle ellipsis which is not easy to find corresponding problems and answers in the standard problem set of teaching text. Ellipsis handling includes ellipsis detection, type classification and recovery. There are many domain specific dialog system, but no one similar ours. Ellipses in our thesis are classified according to omitted element. Medical diagnosis template saves necessary information from dialogs. Our system is a hybrid system, rule-based module and machine learning. Rule-based module uses information from template to detect, classify and recover ellipsis. If some ellipsis can’t be detected by rule-based module, machine learning will implement. We learn a classifier for detecting ellipsis. Features include lexical surface, word information, POS, verb tense, punctuation and special terms from observation. These features also can be combined. After detection, rule-based module classifies and recovers ellipsis. The training and testing data are from virtual patient’s teaching record and medical diagnosis record in the hospital. Our machine learning method is Condition Random Field(CRF). Training is performed in 10-fold-cross-validation. In training, when using best features and feature combination, ellipsis detection classifier with a f-value of 86.73%, then recover by rule-based module with a f-value of 78.95%. Using information in diagnosis template to detect and recover ellipsis with a f-value of 82.58%. Total ellipsis system with a f-value of 85.54%. In testing, ellipsis system with a recall of 77.4%, a precision of 79.36% and a f-value of 78.35%
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M98570020
Appears in Collections:[資訊工程學系] 博碩士論文

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