Interpretable Disease Name Estimation based on Learned Models using Semantic Representation Learning of Medical Terms

Ikuo Keshi, Ryota Daimon, Atsushi Hayashi

Abstract: This paper describes a method for constructing a learned model for estimating disease names using semantic representation learning for medical terms and an interpretable disease-name estimation method based on the model. Experiments were conducted using old and new electronic medical records from Toyama University Hospital, where the data distribution of disease names differs significantly. The F1-score of the disease name estimation was improved by about 10 points compared with the conventional method using a general word semantic vector dictionary with a faster linear SVM. In terms of the interpretability of the estimation, it was confirmed that 70% of the disease names could provide higher-level concepts as the basis for disease name estimation. As a result of the experiments, we confirmed that both interpretability and accuracy for disease name estimation are possible to some extent.

External IDs:dblp:conf/ic3k/KeshiDH22