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Medical events are often infrequent, thus becomes hard to predict. In this paper, we focus on predictor that forecasts whether a medical event would occur in the next year, and analyzes the impact of event's frequency and data size via predictor's performance. In the experiment, we made 1572 predictors for medical events using Medical Insurance Claims (MICs) data from 800,000 participants and 205.8 million claims over 8 years. The result revealed that (a) forecasting error will be increased when predicting low-frequency events, and (b) increasing the number of training dataset reduces errors. This result suggests that increasing data size is a key to solve low frequency problems. However, we still need additional methods to cope with sparse and imbalanced data. [ABSTRACT FROM AUTHOR]

We analyzed the behavior of patient with a focus on patient-sharing based on the methodology of network analysis. We used an administrative healthcare claims database from September of the years 2008-2020 to identify shared patients with hypertension. The patients' behavior of visiting multiple medical facilities was extracted as graphical data, and we calculated density and centrality as indicators to evaluate the structure of the patient sharing network. Our findings indicate that density, reciprocity, and transitivity increased over time, and that centrality and PageRank were correlated. [ABSTRACT FROM AUTHOR]