| Summary: | 碩士 === 淡江大學 === 資訊管理學系碩士在職專班 === 103 === 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.
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