A Most Resource-Consuming Disease Estimation Method from Electronic Claim Data Based on Labeled LDA

نویسندگان

  • Yasutaka Hatakeyama
  • Takahiro Ogawa
  • Hironori Ikeda
  • Miki Haseyama
چکیده

In this paper, we propose a method to estimate the most resource-consuming disease from electronic claim data based on Labeled Latent Dirichlet Allocation (Labeled LDA). The proposed method models each electronic claim from its medical procedures as a mixture of resourceconsuming diseases. Thus, the most resource-consuming disease can be automatically estimated by applying Labeled LDA to the electronic claim data. Although our method is composed of a simple scheme, this is the first trial for realizing estimation of the most resource-consuming disease. key words: most resource-consuming disease, electronic claim, labeled LDA

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عنوان ژورنال:
  • IEICE Transactions

دوره 99-D  شماره 

صفحات  -

تاریخ انتشار 2016