吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 859-865.doi: 10.13229/j.cnki.jdxbgxb20170406

• Orginal Article • Previous Articles     Next Articles

Construction of disease-symptom semantic net for misdiagnosis prompt

HUANG Lan1,2, JI Lin-ying3, YAO Gang4, ZHAI Rui-feng5, BAI Tian1,2   

  1. 1.College of Computer Science and Technology,Jilin University,Changchun 130012,China;
    2.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China;
    3.College of Software,Jilin University,Changchun 130012,China;
    4.Neurological Department,The Second Hospital of Jilin University,Changchun 130041,China;
    5.College of Electronical and Information Engineering,Changchun University of Science and Technology,Changchun 130022,China
  • Received:2017-04-21 Online:2018-05-20 Published:2018-05-20

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Abstract: A Disease-Symptom Semantic Net (DSSN) for misdiagnosis prompt is constructed. First, symptom words are recognized from medical corpus, and a symptom ontology based on semantic relations between symptom words is established. Then, the relations between diseases and symptoms and the misdiagnosed relations between diseases were test mined and extracted to construct DSSN. DSSN contains Disease Ontology (DO), new established symptom ontology, misdiagnosed relations between diseases and differential diagnosis knowledge. Finally, a use case in clinical diagnosis is used to illustrate that DSSN is helpful to prompt misdiagnosis in clinical assistance diagnosis system.

Key words: artificial intelligence, semantic network, text mining, misdiagnosis, ontology

CLC Number: 

  • TP182
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