| Summary: | 碩士 === 國立成功大學 === 醫學資訊研究所 === 96 === Evidence is more and more important in the medical domain. It is not suitable to solve problems by experience all the time for patient care. Instead of this, the clinician has to apply the best clinical study results to patient care and promote the quality of patient care by fulfilling the Evidence-Based Medicine (EBM). There are many medical literature databases to provide search the related medical evidence these days. Among these, the Medline provides the most complete and rich medical literature. Yet, the clinician often spends much time reading and filtering the literature in face of the miscellaneous database. The clinician can acquire the evidence quickly from the EBM databases, but the EBM databases can’t hold the complete information like the primary databases and the update speed of the EBM database is slower than the primary databases. In this thesis, we developed a document summarization system by extracting the PICO from the clinical medical articles. The P stands for Population. It means the information regarding patients; the I stands for Intervention. It means offering the agents or the clinician’s acts of dealing with the patient’s problem; the C stands for Comparison. It means the alternative intervention; the O stands for Outcome. It means the effects of the intervention. Hope let the clinician realize the main ideas in the citation more quickly. The strategy proposed in this thesis is to analyze the structure of the abstract and parser citation semantically at first. And based on this, exploit the key phrases, patterns, and local contexts to extract the PICO.
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