Hierarchical prescription pattern analysis with symptom labels

Identification of prescription patterns is a useful and interesting goal from multiple perspectives. The identified prescription patterns may expand the horizons of medical knowledge, and may be evaluated by subject matter experts to label certain patterns as anomalies calling for further investigation; for example, in prescription costs for insurance companies. This paper presents the statistical modeling details of the tag hierarchical topic model (Tag-HTM) and its application to the Health Insurance Review & Assessment Service (HIRA) dataset. The implementation of Tag-HTM revealed a hierarchical structure for medicine symptom distributions, which could constitute a new hierarchical categorization for diseases. The experimental results demonstrate that our generated hierarchical structure can replicate the existing hierarchy, namely ICD-10, which has been created by medical subject matter experts, to a considerable extent. Furthermore, the experiments indicate a quantitative performance improvement; that is, the superior perplexities of Tag-HTM compared to baselines. Moreover, Tag-HTM was able to isolate prescription patterns with higher medical costs as a branch of hierarchical clustering, and this cluster could form a prescription collection of interest to subject matter experts in insurance companies.