首页|University of Florida Reports Findings in COVID-19 (Identifying Potential Factor s Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data)
University of Florida Reports Findings in COVID-19 (Identifying Potential Factor s Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Coronavirus - COVID-19 is the subject of a report. According to news originating from Gainesville, Flo rida, by NewsRx correspondents, research stated, "Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients e ndured worse outcomes disproportionately compared with non-Hispanic White patien ts, but the epidemiological basis for these observations was complex and multifa ceted." Our news journalists obtained a quote from the research from the University of F lorida, "This study aimed to elucidate the potential reasons behind the worse ou tcomes of COVID-19 experienced by non- Hispanic Black patients compared with non- Hispanic White patients and how these variables interact using an explainable ma chine learning approach. In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's da ta trust of health care recipients in Florida through April 28, 2021. We assesse d the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cas es. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machin e learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Compared to non-Hispanic Whit e patients, non-Hispanic Blacks patients were younger, more likely to be uninsur ed, had a higher prevalence of emergency department and inpatient visits, and we re in regions with higher area deprivation index rankings and pollutant concentr ations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking hig hest in non-Hispanic White patients. However, for non-Hispanic Black patients, c ongestive heart failure was a primary predictor. Other variables, such as food e nvironment measures and air pollution indicators, also ranked high. By consolida ting comorbidities into the Elixhauser Comorbidity Index, this became the top pr edictor, providing a comprehensive risk measure. The study reveals that individu al and geo-socioeconomic factors significantly influence the outcomes of COVID-1 9. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and stat istical testing are needed to fully substantiate these observations."
GainesvilleFloridaUnited StatesNor th and Central AmericaAir PollutionCOVID-19COVID-19 ModelClinical Resear chClinical Trials and StudiesCoronavirusCyborgsDisease ModelEmerging T echnologiesEpidemiologyHealth and MedicineHospitalsMachine LearningMor talityRNA VirusesRisk and PreventionSARS-CoV-2Severe Acute Respiratory S yndrome Coronavirus 2ViralVirology
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