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Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies

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dc.contributor.authorWei Kit Looen_US
dc.contributor.authorKhairunnisa Hasikinen_US
dc.contributor.authorAnwar Suhaimien_US
dc.contributor.authorPor Lip Yeeen_US
dc.contributor.authorKareen Teoen_US
dc.contributor.authorKaijian Xiaen_US
dc.contributor.authorPengjiang Qianen_US
dc.contributor.authorYizhang Jiangen_US
dc.contributor.authorYuanpeng Zhangen_US
dc.contributor.authorSamiappan Dhanalakshmien_US
dc.contributor.authorMuhammad Mokhzaini Azizanen_US
dc.contributor.authorKhin Wee Laien_US
dc.date.accessioned2024-05-28T05:49:47Z
dc.date.available2024-05-28T05:49:47Z
dc.date.issued2022
dc.date.submitted2022-11-11
dc.descriptionFront. Public Health 10:898254.en_US
dc.description.abstractIn this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “Sars-CoV-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.en_US
dc.identifier.citationLoo WK, Hasikin K, Suhaimi A, Yee PL, Teo K, Xia K, Qian P, Jiang Y, Zhang Y, Dhanalakshmi S, Azizan MM and Lai KW (2022) Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies. Front. Public Health 10:898254. doi: 10.3389/fpubh.2022.898254en_US
dc.identifier.doi10.3389/fpubh.2022.898254
dc.identifier.epage11
dc.identifier.issn2296-2565
dc.identifier.issue2022
dc.identifier.other2494-14
dc.identifier.spage1
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fpubh.2022.898254/full
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/6620
dc.identifier.volume10
dc.language.isoenen_US
dc.publisherFRONTIERSen_US
dc.relation.ispartofFrontiers in Public Healthen_US
dc.subjectCOVID-19, readmission, risk factors, mortality, machine learningen_US
dc.titleSystematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studiesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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