Keyword extraction and structuralization of medical reports

Purpose: In recent years, patients usually accept more accurate and detailed examinations because of the rapid advances in medical technology. Many of the examination reports are not represented in numerical data, but text documents written by the medical examiners based on the observations from the instruments and biochemical tests. If the above-mentioned unstructured data can be organized as a report in a structured form, it will help doctors to understand a patient's status of the various examinations more efficiently. Besides, further association analysis on the structuralized data can be performed to identify potential factors that affect a disease. Methods: In this paper, from the pathology examination reports of renal diseases, we applied the POS tagging results of natural language analysis to automatically extract the keyword phrases. Then a medical dictionary for various examination items in an examination report is established, which is used as the basic information for retrieving the terms to construct a structured form of the report. Moreover, a topical probability modeling method is applied to automatically discover the candidate keyword phrases of the examination items from the reports. Finally, a system is implemented to generate the structured form for the various examination items in a report according to the constructed medical dictionary. Results and conclusion: The results of the experiments showed that the methods proposed in this paper can effectively construct a structural form of examination reports. Furthermore, the keywords of the popular examination items can be extracted correctly. The above techniques will help automatic processing and analysis of medical text reports.