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dc.contributor.authorCheng, Yi-Tingen_US
dc.contributor.authorLin, Yu-Fengen_US
dc.contributor.authorChiang, Kuo-Hwaen_US
dc.contributor.authorTseng, Vincent S.en_US
dc.date.accessioned2018-08-21T05:53:51Z-
dc.date.available2018-08-21T05:53:51Z-
dc.date.issued2017-03-01en_US
dc.identifier.issn2168-2194en_US
dc.identifier.urihttp://dx.doi.org/10.1109/JBHI.2017.2657802en_US
dc.identifier.urihttp://hdl.handle.net/11536/145242-
dc.description.abstractChronic diseases have been among the major concerns in medical fields since they may cause a heavy burden on healthcare resources and disturb the quality of life. In this paper, we propose a novel framework for early assessment on chronic diseases by mining sequential risk patterns with time interval information from diagnostic clinical records using sequential rules mining, and classification modeling techniques. With a complete workflow, the proposed framework consists of four phases namely data preprocessing, risk pattern mining, classification modeling, and post analysis. For empiricasl evaluation, we demonstrate the effectiveness of our proposed framework with a case study on early assessment of COPD. Through experimental evaluation on a large-scale nationwide clinical database in Taiwan, our approach can not only derive rich sequential risk patterns but also extract novel patterns with valuable insights for further medical investigation such as discovering novel markers and better treatments. To the best of our knowledge, this is the first work addressing the issue of mining sequential risk patterns with time-intervals as well as classification models for early assessment of chronic diseases.en_US
dc.language.isoen_USen_US
dc.subjectData miningen_US
dc.subjectdisease risk assessmenten_US
dc.subjectearly predictionen_US
dc.subjectelectronic medical recordsen_US
dc.subjectsequential patternsen_US
dc.titleMining Sequential Risk Patterns From Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Diseaseen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/JBHI.2017.2657802en_US
dc.identifier.journalIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICSen_US
dc.citation.volume21en_US
dc.citation.spage303en_US
dc.citation.epage311en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000397626200003en_US
Appears in Collections:Articles