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dc.contributor.authorChin, Chu-Yuen_US
dc.contributor.authorHsieh, Sun-Yuanen_US
dc.contributor.authorTseng, Vincent S.en_US
dc.date.accessioned2019-04-02T05:59:05Z-
dc.date.available2019-04-02T05:59:05Z-
dc.date.issued2018-11-26en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0207579en_US
dc.identifier.urihttp://hdl.handle.net/11536/148516-
dc.description.abstractRecently, a number of analytical approaches for probing medical databases have been developed to assist in disease risk assessment and to determine the association of a clinical condition with others, so that better and intelligent healthcare can be provided. The early assessment of disease risk is an emerging topic in medical informatics. If diseases are detected at an early stage, prognosis can be improved and medical resources can be used more efficiently. For example, if rheumatoid arthritis (RA) is detected at an early stage, appropriate medications can be used to prevent bone deterioration. In early disease risk assessment, finding important risk factors from large-scale medical databases and performing individual disease risk assessment have been challenging tasks. A number of recent studies have considered risk factor analysis approaches, such as association rule mining, sequential rule mining, regression, and expert advice. In this study, to improve disease risk assessment, machine learning and matrix factorization techniques were integrated to discover important and implicit risk factors. A novel framework is proposed that can effectively assess early disease risks, and RA is used as a case study. This framework comprises three main stages: data preprocessing, risk factor optimization, and early disease risk assessment. This is the first study integrating matrix factorization and machine learning for disease risk assessment that is applied to a nation-wide and longitudinal medical diagnostic database. In the experimental evaluations, a cohort established from a large-scale medical database was used that included 1007 RA-diagnosed patients and 921,192 control patients examined over a nine-year follow-up period (2000-2008). The evaluation results demonstrate that the proposed approach is more efficient and stable for disease risk assessment than state-of-the-art methods.en_US
dc.language.isoen_USen_US
dc.titleeDRAM: Effective early disease risk assessment with matrix factorization on a large-scale medical database: A case study on rheumatoid arthritisen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0207579en_US
dc.identifier.journalPLOS ONEen_US
dc.citation.volume13en_US
dc.contributor.department資訊科學與工程研究所zh_TW
dc.contributor.departmentInstitute of Computer Science and Engineeringen_US
dc.identifier.wosnumberWOS:000451325700040en_US
dc.citation.woscount0en_US
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