論文

査読有り 国際誌
2019年8月21日

Extracting Symptom Names and Disease-Symptom Relationships from Web Texts Using a Multi-Column Convolutional Neural Network.

Studies in health technology and informatics
  • Shoya Wada
  • ,
  • Ryu Iida
  • ,
  • Kentaro Torisawa
  • ,
  • Toshihiro Takeda
  • ,
  • Shiro Manabe
  • ,
  • Yasushi Matsumura

264
開始ページ
423
終了ページ
427
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3233/SHTI190256

We propose a method to create large-scale Japanese medical dictionaries that include symptom names and information about the relationship between a disease and its symptoms using a large web archive that includes large amounts of text written by non-medical experts. Our goal is to develop a diagnosis support system that makes a diagnosis according to the natural language (NL) inputs provided by patients. To achieve this, two medical dictionaries need to be constructed: one that includes a wide variety of symptom names expressed in NL and another that includes information about the relationship between a disease and its symptoms. Dictionaries will then be used to predict the patient's disease via two developed methods that extract symptom names and disease-symptom relationships. Both methods retrieve sentences using WISDOM X and then apply neural classifiers to them. Our experimental results show that our methods achieved 93.8% and 88.3% in the F1-score, respectively.

リンク情報
DOI
https://doi.org/10.3233/SHTI190256
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/31437958
ID情報
  • DOI : 10.3233/SHTI190256
  • PubMed ID : 31437958

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