Validating the representation of distance between infarct diseases using word embedding

Abstract

Abstract Background The pivot and cluster strategy (PCS) is a diagnostic reasoning strategy that automatically elicits disease clusters similar to a differential diagnosis in a batch. Although physicians know empirically which disease clusters are similar, there has been no quantitative evaluation. This study aimed to determine whether inter-disease distances between word embedding vectors using the PCS are a valid quantitative representation of similar disease groups in a limited domain. Methods Abstracts were extracted from the Ichushi Web database and subjected to morphological analysis and training using Word2Vec, FastText, and GloVe. Consequently, word embedding vectors were obtained. For words including “infarction,” we calculated the cophenetic correlation coefficient (CCC) as an internal validity measure and the adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) with ICD-10 codes as the external validity measures. This was performed for each combination of metric and hierarchical clustering method. Results Seventy-one words included “infarction,” of which 38 diseases matched the ICD-10 standard with the appearance of 21 unique ICD-10 codes. When using Word2Vec, the CCC was most significant at 0.8690 (metric and method: euclidean and centroid), whereas the AMI was maximal at 0.4109 (metric and method: cosine and correlation, and average and weighted). The NMI and ARI were maximal at 0.8463 and 0.3593, respectively (metric and method: cosine and complete). FastText and GloVe generally resulted in the same trend as Word2Vec, and the metric and method that maximized CCC differed from the ones that maximized the external validity measures. Conclusions The metric and method that maximized the internal validity measure differed from those that maximized the external validity measures; both produced different results. The cosine distance should be used when considering ICD-10, and the Euclidean distance when considering the frequency of word occurrence. The distributed representation, when trained by Word2Vec on the “infarction” domain from a Japanese academic corpus, provides an objective inter-disease distance used in PCS.

Keywords

• Infarct disease
• Word2Vec
• Pivot and cluster strategy
• Clustering
• ICD-10