Machine Learning based Prediction of DPC from Discharge Summaries

DOI
  • KIMURA Tomohiro
    Division of Medical Service, Faculty of Medicine, Shimane University
  • TSUMOTO Shusaku
    Department of Medical Informatics, Faculty of Medicine, Shimane University
  • HIRANO Shoji
    Department of Medical Informatics, Faculty of Medicine, Shimane University

Bibliographic Information

Other Title
  • 機械学習による退院時要約からのDPC分類の推測

Description

<p>This paper proposes a method for construction of classifiers for discharge summaries, composed of the following five steps First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspondence analysis is applied to the classification labels and the term matrix and generates two dimensional coordinates for all the terms and labels. Third, by measuring the distances between categories and the terms, ranking of key words is generated. Fourthly, keywords are selected as attributes according to the ranks, and training examples for classifiers will be generated. Finally, machine learning methods are applied to the training examples. Experimental validation shows that random forest achieved the best performance and the second best was the deep learners, but decision tree methods with many keywords performed only a little worse than neural network or deep learning methods.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390566775142729728
  • NII Article ID
    130007856905
  • DOI
    10.11517/pjsai.jsai2020.0_2i1gs204
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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