Auto-Selection of DPC Codes from Discharge Summaries by Text Mining in Several Hospitals and Analysis of Differences in Discharge Summaries
Shunsuke Doi*1, Takahiro Suzuki*2, Gen Shimada*3,
Mitsuhiro Takasaki*4, Shinsuke Fujita*5,
Toshiyo Tamura*1, and Katsuhiko Takabayashi*2
*1Graduate School of Engineering, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8677, Japan
*2Department of Medical Informatics and Management, Chiba University Hospital, Japan
*3Medical Information Center, St. Luke’s International Hospital, Japan
*4Division of Medical Informatics, Saga University Hospital, Japan
*5Department of Welfare and Medical Intelligence, Chiba University Hospital, Japan
Recently, Electronic Medical Record (EMR) systems have become popular in Japan, and numerous discharge summaries are being stored electronically, although they have not yet been reutilized. We performed text mining by using the term frequencyinverse document frequency method along with a morphological analysis of the discharge summaries from 3 hospitals (the Chiba University Hospital, St. Luke’s International Hospital, and the Saga University Hospital). We found differences in the styles of the summaries between hospitals, while the rates of properly classified Diagnosis Procedure Combination (DPC) codes were almost the same. Beyond the different styles for the discharge summaries, the text mining method was able to obtain appropriate extracts of the proper DPC codes. An improvement was observed by using the integrated model data between the hospitals. It appeared that a large database containing data from many hospitals could improve the precision of text mining.
Mitsuhiro Takasaki, Shinsuke Fujita,
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