Knowledge Discovery in Multivariate Clinical Time Series based on Hierarchical Structure Modeling

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It has been required to automatically discover useful knowledge for symptom recognition and treatment in clinical data on chronic disease, which is a set of multivariate time series. This research project aims to develop methods of modeling, feature extraction, and prediction of symptoms for clinical examination and treatment histories on chronic hepatitis C. We described symptoms by hierarchically applying autoregressive models and autoregressive conditional heteroscedastic models, extracted features using the modeling results, and derived knowledge on how much measurements are necessary for symptom recognition based on the model orders. In addition to that, we developed a system to predict the degree of liver damage using kernel logistic regression and proposed a new classifier that is based on kernel logistic regression and trains itself with discriminative learning.