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Article: Tensor Factorization-Based Prediction with an Application to Estimate the Risk of Chronic Diseases

TitleTensor Factorization-Based Prediction with an Application to Estimate the Risk of Chronic Diseases
Authors
Keywordsdata mining
health
health care
Medical information systems
Issue Date2021
Citation
IEEE Intelligent Systems, 2021, v. 36, n. 6, p. 53-61 How to Cite?
AbstractTensor factorization has emerged as a powerful method to address the challenges of high dimensionality and sparsity regarding disease development and comorbidity. Chronic diseases have a high likelihood to co-occur, such that patients suffering from one chronic disease have an elevated risk for other diseases in the course of aging. Despite rich results of risk assessment models for chronic diseases, risk prediction considering the complex mechanisms of disease development and comorbidity remains to be underresearched. This research aims to develop tensor factorization-based methods to predict the onset of new chronic diseases through incorporating the comorbidity patterns with the clinical and sequential factors revealed in the electronic health records (EHRs). The efficacy of the proposed methods was validated through predicting the onset of new chronic diseases using the EHRs data for 23 years from a major hospital in Hong Kong. The proposed methods could inform proactive health management programs for at-risk patients with different chronic conditions.
Persistent Identifierhttp://hdl.handle.net/10722/330448
ISSN
2023 Impact Factor: 5.6
2023 SCImago Journal Rankings: 2.195
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Haolin-
dc.contributor.authorZhang, Qingpeng-
dc.contributor.authorChen, Frank Youhua-
dc.contributor.authorLeung, Eman Yee Man-
dc.contributor.authorWong, Eliza Lai Yi-
dc.contributor.authorYeoh, Eng Kiong-
dc.date.accessioned2023-09-05T12:10:45Z-
dc.date.available2023-09-05T12:10:45Z-
dc.date.issued2021-
dc.identifier.citationIEEE Intelligent Systems, 2021, v. 36, n. 6, p. 53-61-
dc.identifier.issn1541-1672-
dc.identifier.urihttp://hdl.handle.net/10722/330448-
dc.description.abstractTensor factorization has emerged as a powerful method to address the challenges of high dimensionality and sparsity regarding disease development and comorbidity. Chronic diseases have a high likelihood to co-occur, such that patients suffering from one chronic disease have an elevated risk for other diseases in the course of aging. Despite rich results of risk assessment models for chronic diseases, risk prediction considering the complex mechanisms of disease development and comorbidity remains to be underresearched. This research aims to develop tensor factorization-based methods to predict the onset of new chronic diseases through incorporating the comorbidity patterns with the clinical and sequential factors revealed in the electronic health records (EHRs). The efficacy of the proposed methods was validated through predicting the onset of new chronic diseases using the EHRs data for 23 years from a major hospital in Hong Kong. The proposed methods could inform proactive health management programs for at-risk patients with different chronic conditions.-
dc.languageeng-
dc.relation.ispartofIEEE Intelligent Systems-
dc.subjectdata mining-
dc.subjecthealth-
dc.subjecthealth care-
dc.subjectMedical information systems-
dc.titleTensor Factorization-Based Prediction with an Application to Estimate the Risk of Chronic Diseases-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/MIS.2021.3071018-
dc.identifier.scopuseid_2-s2.0-85103895299-
dc.identifier.volume36-
dc.identifier.issue6-
dc.identifier.spage53-
dc.identifier.epage61-
dc.identifier.eissn1941-1294-
dc.identifier.isiWOS:000733340300013-

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