簡易疾患知識ベースを基にした疾患間の類似度計算

抄録

For clinical decision support systems designed to help physicians to make diagnostic decisions, "disease similarity" data is highly valuable in that they realize continuous presentation of diagnostic candidates. Toward such a recommendation system, calculation of disease similarity between diseases is a key component, and thus, this paper explores the method to measure the similarity on a simplified disease knowledge base. Our disease knowledge base comprises disease master data, symptom master data, and disease-symptom relations that include clinical information of 1550 disorders. The calculation of the disease similarity is performed on this knowledge base, with i) disease classification, ii) probabilistic calculation, and iii) machine learning, and the results are evaluated with a gold standard list audited by a physician. A comparative study revealed that the machine learning approach outperforms the others. The result suggests that even a superficial calculation on a simplified knowledge base can satisfy the clinical needs in this problem domain.