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Finding Clinical Knowledge from MEDLINE Abstracts by Text Summarization Technique

Today, the MEDLINE is an important repository containing more than 26 million citations and abstracts in the fields of medicine, while PubMed provides free access to MEDLINE and links to full-text articles. MEDLINE abstracts becomes a potential source of new knowledge in medical field. However, it is time-consuming and labour-intensive to find knowledge from MEDLINE abstracts, when a search returns much abstracts and each may contain a large volume of information. Therefore, this work aims to present a method of summarizing clinical knowledge from a MEDLINE abstract. The main mechanisms of the proposed method are driven on natural language processing (NLP) and text filtering techniques. The case study of this work is to summarize the clinical knowledge from a MEDLINE abstracts relating to cervical cancer in clinical trials. In the evaluation stage, the actual results obtained from a domain expert are used to compare the predicted results. After testing by recall, precision, and F-score, they return the satisfactory results, where the average of recall, precision, and F-measure are 0.84, 1.00, and 0.91 respectively.