Sungrae Park

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Analyzing patient records is important for improving the quality of medical services and for understanding each patient’s historical diseases. However, the huge size of the data requires statistical analysis procedures. In this paper, we proposed a probabilistic model—the disease-medicine topic model (DMTM)—to explore connected knowledge about diseases and(More)
Recently, training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has improved the generalization performance of neural networks. In contrast to the biased individual inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize(More)
Wide variance exists among individuals and institutions for treating patients with medicine. This paper analyzes prescription patterns using a topic model with more than four million prescriptions. Specifically, we propose the disease-medicine pattern model (DMPM) to extract patterns from a large collection of insurance data by considering disease codes(More)
Understanding the emergence of collective emotions is critical to the analysis of online and offline societies. The agentbased simulation community has developed various social norm models to see the polarization of collective emotions. Yet, a few models have psychological background as fundamentals, as well as statistical validation, and this paper aims at(More)
Identifying the prescription patterns would be a useful and interesting goal from multiple perspectives. Firstly, the identified patterns could expand the horizon of the medical practice knowledge. Secondly, the identified prescription patterns can be evaluated by subject-matter experts to label some of the patterns as anomaly calling for further(More)
A series of events generates multiple types of time series data, such as numeric and text data over time, and the variations of the data types capture the events from different angles. This paper aims to integrate the analyses on such numerical and text time-series data influenced by common events with a single model to better understand the events.(More)
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