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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105530

    Title: 電子醫囑時序資訊擷取之研究
    Other Titles: A study of temporal information extraction on computerized physician order
    Authors: 蕭詩寰;Hsiao, Shih-Huan
    Contributors: 淡江大學資訊管理學系碩士在職專班
    戴敏育;Day, Min-Yuh
    Keywords: 電子醫囑;時序資訊識別;科技接受模式;資訊系統成功模式;Electronic prescription;temporal information identification;TAM;D&M
    Date: 2015
    Issue Date: 2016-01-22 14:58:24 (UTC+8)
    Abstract: 對研究疾病的治療來說,電子醫囑中的時序資訊極為重要。時序資訊可分為日期、時間、頻率、期間四種主要類型。本研究分為兩主要部分。在第一部分中,我們實作了一套時序資訊識別系統。利用命名實體辨識技術,自電子醫囑的自然語言語句中,辨認出時序資訊,並加以分類並正規化。因應四類時序資訊,我們採用條件隨機域機器學習方法、Stanford CoreNLP工具及規則式方法並行運作,在國際知名的i2b2資料集上,可達到80%的F1-Measure。在第二部分中,我們採用科技接受模式與資訊系統成功模式兩種模型分析標示時序資訊的醫囑輸入系統,並邀請醫院護理師參與使用者調查。結果顯示,護理師普遍認同系統標示時序資訊的新功能。在管理意涵上,當純文字的醫囑標示出時序資訊,可帶來下列好處:(1) 護理部門管理階層可使用時序資訊來分析護理師工作內容,稽核護理師工作的品質。(2) 標示時序資訊可提高護理師對醫囑的理解度,確保病人受到正確地治療與照護。(3) 可用於醫囑執行完成率的計算。本研究成功地結合資訊系統實作與管理層面分析,對此議題之後續研究,將有重要的參考價值。
    For treatment of diseases, temporal information in electronic prescriptions is extremely important. Temporal information can be divided into four main types: the date, time, frequency, period. This study has two main parts. In the first part, we construct a temporal named entity identification system that recognize temporal named entities from natural language sentences in electronic prescriptions and normalize these named entities. By hybridizing a CRF-based system, the Stanford CoreNLP system and our rule-based system, we achieve an F1-Measure of 80% on the well-known i2b2 dataset. In the second part, we use two models TAM and D&M to analyze the user acceptance of adding temporal information to physician order input systems. Over 50 hospital nurses were invited to participate in our user study. The results show that nurse practitioners generally agree that adding temporal information can improve their working efficiency. In management perspectives, highlighted temporal information brings the following benefits. (1) For care sector managers, temporal information can be used to analyze the working quality of nurses. (2) For nurses, highlighted temporal information can improve their understanding of doctor''s orders, ensuring that patients receive proper treatment and care. (3) Identified temporal information can be used to calculate the completion rate of prescription orders. This study successfully combines the construction of an information system and the analysis in management perspectives, presenting the great reference value for follow-up studies in this topic.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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