完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Cheng, Yi-Ting | en_US |
dc.contributor.author | Lin, Yu-Feng | en_US |
dc.contributor.author | Chiang, Kuo-Hwa | en_US |
dc.contributor.author | Tseng, Vincent S. | en_US |
dc.date.accessioned | 2018-08-21T05:53:51Z | - |
dc.date.available | 2018-08-21T05:53:51Z | - |
dc.date.issued | 2017-03-01 | en_US |
dc.identifier.issn | 2168-2194 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1109/JBHI.2017.2657802 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/145242 | - |
dc.description.abstract | Chronic 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.iso | en_US | en_US |
dc.subject | Data mining | en_US |
dc.subject | disease risk assessment | en_US |
dc.subject | early prediction | en_US |
dc.subject | electronic medical records | en_US |
dc.subject | sequential patterns | en_US |
dc.title | Mining Sequential Risk Patterns From Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/JBHI.2017.2657802 | en_US |
dc.identifier.journal | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | en_US |
dc.citation.volume | 21 | en_US |
dc.citation.spage | 303 | en_US |
dc.citation.epage | 311 | en_US |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | Department of Computer Science | en_US |
dc.identifier.wosnumber | WOS:000397626200003 | en_US |
顯示於類別: | 期刊論文 |