2013 Fiscal Year Final Research Report
Knowledge Discovery in Multivariate Clinical Time Series based on Hierarchical Structure Modeling
| Project/Area Number |
23500190
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Doshisha University |
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Principal Investigator |
OHSAKI Miho 同志社大学, 理工学部, 准教授 (30313927)
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| Project Period (FY) |
2011 – 2013
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| Keywords | 知識発見 / 多変量時系列 / 検査治療履歴 |
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| Research Abstract |
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.
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Research Products
(13 results)
