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Stud Health Technol Inform. 2019 Aug 21;264:423-427. doi: 10.3233/SHTI190256.

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

Author information

Department of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
National Institute of Information and Communications Technology, Seika, Kyoto, Japan.


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.


Automated; Machine learning; Natural language processing; Pattern Recognition

[Indexed for MEDLINE]

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