複数の機械学習手法を用いた退院時サマリからの自動DPCコーディング

DOI Web Site
  • 岡本 和也
    京都大学医学部附属病院医療情報部
  • 内山 俊郎
    日本電信電話株式会社サイバーソリューション研究所
  • 竹村 匡正
    京都大学医学部附属病院医療情報部
  • 足立 貴行
    日本電信電話株式会社サイバーソリューション研究所
  • 粂 直人
    京都大学医学部附属病院医療情報部
  • 黒田 知宏
    京都大学医学部附属病院医療情報部
  • 内山 匡
    日本電信電話株式会社サイバーソリューション研究所
  • 吉原 博幸
    京都大学医学部附属病院医療情報部

書誌事項

タイトル別名
  • Automatic DPC Code Selection from Discharge Summaries Using Several Machine Learning Methods
  • フクスウ ノ キカイ ガクシュウ シュホウ オ モチイタ タイインジ サマリ カラ ノ ジドウ DPC コーディング

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抄録

A DPC code expresses a primary disease, a complication, and procedures, etc. In 2010, 1334 hospitals use DPC codes for calculations of medical fees. Since, in the hospitals, the medical fee of each case is calculated based on one DPC code, each case must be classified into one DPC code. However, the classification is difficult in some cases because patients sometimes have various conditions. Therefore, automatic DPC code selections using machine learning are being studied. Suzuki et al. evaluated automatic DPC code selections from discharge summaries using a vector space method. However, there are general machine learning methods except for the vector space method. Hence, we must evaluate other machine learning methods exhaustively for improvement of accuracy of automatic DPC code selections. Therefore, we evaluated automatic DPC code selections from discharge summaries using naïve Bayes method, SVM, concept base method, and another vector space method which is different from the vector space model used by Suzuki et al. We considered these machine learning methods as general ones. We also focus on characteristics of each machine learning methods on automatic DPC code selections and we utilize a method which combines some machine learning methods. First, the combining method estimates confidences of the machine learning methods bases on classification scores that the machine learning methods regard as classification evidence. Next, the combining method adopts the method whose confidence is highest. We compared accuracy of the methods using discharge summaries created in 2008 fiscal year in Kyoto University Hospital. As a result, SVM classified 72.2% of the cases into correct DPC codes though the vector space model utilized by Suzuki et al. classified 64.8% into correct DPC codes. Moreover the combining method classified 76.1% into correct DPC codes. In conclusion, we achieved significant improvement.

収録刊行物

  • 生体医工学

    生体医工学 49 (1), 40-47, 2011

    公益社団法人 日本生体医工学会

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