دورية أكاديمية
An Unsupervised Cluster Analysis of Post-COVID-19 Mental Health Outcomes and Associated Comorbidities.
| العنوان: | An Unsupervised Cluster Analysis of Post-COVID-19 Mental Health Outcomes and Associated Comorbidities. |
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| المؤلفون: | Brown KA; Center for Biomedical Informatics, Brown University, Providence RI., Sarkar IN; Center for Biomedical Informatics, Brown University, Providence RI.; Rhode Island Quality Institute, Providence RI., Crowley KM; Center for Biomedical Informatics, Brown University, Providence RI., Aluthge DP; Center for Biomedical Informatics, Brown University, Providence RI.; Rhode Island Quality Institute, Providence RI., Chen ES; Center for Biomedical Informatics, Brown University, Providence RI. |
| المصدر: | AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2023 Apr 29; Vol. 2022, pp. 289-298. Date of Electronic Publication: 2023 Apr 29 (Print Publication: 2022). |
| نوع المنشور: | Journal Article; Research Support, N.I.H., Extramural |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Medical Informatics Association Country of Publication: United States NLM ID: 101209213 Publication Model: eCollection Cited Medium: Internet ISSN: 1942-597X (Electronic) Linking ISSN: 15594076 NLM ISO Abbreviation: AMIA Annu Symp Proc Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Bethesda, MD : American Medical Informatics Association, c2003- |
| مواضيع طبية MeSH: | COVID-19*, Humans ; Retrospective Studies ; Pandemics ; COVID-19 Testing ; Comorbidity ; Cluster Analysis ; Outcome Assessment, Health Care |
| مستخلص: | The COVID-19 pandemic continues to be widespread, and little is known about mental health impacts from dealing with the disease itself. This retrospective study used a deidentified health information exchange (HIE) dataset of electronic health record data from the state of Rhode Island and characterized different subgroups of the positive COVID-19 population. Three different clustering methods were explored to identify patterns of condition groupings in this population. Increased incidence of mental health conditions was seen post-COVID-19 diagnosis, and these individuals exhibited higher prevalence of comorbidities compared to the negative control group. A self-organizing map cluster analysis showed patterns of mental health conditions in half of the clusters. One mental health cluster revealed a higher comorbidity index and higher severity of COVID-19 disease. The clinical features identified in this study motivate the need for more in-depth analysis to predict and identify individuals at high risk for developing mental illness post-COVID-19 diagnosis. (©2022 AMIA - All rights reserved.) |
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| معلومات مُعتمدة: | T32 GM128596 United States GM NIGMS NIH HHS; U54 GM104942 United States GM NIGMS NIH HHS; U54 GM115677 United States GM NIGMS NIH HHS |
| تواريخ الأحداث: | Date Created: 20230502 Date Completed: 20230503 Latest Revision: 20230826 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC10148293 |
| PMID: | 37128434 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1942-597X |
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