C. Everett Koop Healthcare System for Biosecurity and Defense DOI

Haley R. Warzecha,

Alison Podsednik,

Joseph M. Rosen

и другие.

Springer eBooks, Год журнала: 2024, Номер unknown, С. 165 - 192

Опубликована: Янв. 1, 2024

Язык: Английский

Diabetes Does Not Increase the Risk of Hospitalization Due to COVID-19 in Patients Aged 50 Years or Older in Primary Care—APHOSDIAB—COVID-19 Multicenter Study DOI Open Access
Domingo Orozco‐Beltrán, Juan Francisco Merino-Torres, Antonio Pérez

и другие.

Journal of Clinical Medicine, Год журнала: 2022, Номер 11(8), С. 2092 - 2092

Опубликована: Апрель 8, 2022

The purpose of this study was to identify clinical, analytical, and sociodemographic variables associated with the need for hospital admission in people over 50 years infected SARS-CoV-2 assess whether diabetes mellitus conditions risk hospitalization. A multicenter case-control analyzing electronic medical records patients COVID-19 from 1 March 2020 30 April 2021 conducted. We included 790 patients: 295 cases admitted 495 controls. Under half (

Язык: Английский

Процитировано

3

Predicting Post-Liver Transplant Outcomes in Patients with Acute-on-Chronic Liver Failure using Expert-Augmented Machine Learning DOI Creative Commons
Jin Ge, Jean Digitale, Cynthia Fenton

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Март 5, 2023

Abstract Background Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF) but up to 40% mortality post-LT has been reported. Existing models in ACLF have limited by small samples. In this study, we developed novel Expert-Augmented Machine Learning (EAML) model predict outcomes. Methods We identified patients the University of California Health Data Warehouse (UCHDW). used EAML, which uses RuleFit machine learning (ML) algorithm extract rules from decision-trees that are then evaluated human experts, compared EAML/RuleFit’s performances versus other popular models. Results 1,384 patients. For death at one-year: areas-under-the-receiver-operating characteristic curve (AUROCs) were 0.707 (Confidence Interval [CI] 0.625-0.793) EAML and 0.719 (CI 0.640-0.800) RuleFit. 90-days: AUROCs 0.678 0.581-0.776) 0.615-0.800) pairwise comparisons, EAML/RuleFit outperformed cross-sectional Divergences between experts ML rankings revealed biases artifacts underlying data. Conclusions Significant discrepancies occurred biomarkers clinical practice. may serve as method ML-guided hypothesis generation further research.

Язык: Английский

Процитировано

1

Identifying direct risk factors in UK Biobank with simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink DOI Creative Commons
Nicolas Arning, Helen Fryer, Daniel J. Wilson

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 2, 2024

Abstract Big data approaches to discovering non-genetic risk factors have lagged behind genome-wide association studies that routinely uncover novel genetic for diverse diseases. Instead, epidemiology typically focuses on candidate factors. Since modern biobanks contain thousands of potential factors, may introduce bias, inadequately control multiple testing, and miss important signals. Bayesian model averaging offers a solution, but classical statistics predominates, perhaps because concern the prior unduly influences results. Here we show simultaneous frequentist discovery direct is possible via model-averaged hypothesis testing approach large samples called ‘Doublethink’. Doublethink produces interchangeable posterior odds p -values false rate (FDR) familywise error (FWER). We implement in R apply it discover COVID-19 hospitalization 2020 among 1,912 variables UK Biobank. find nine exposome-wide significant at 9% FDR 0.05% FWER. These include several commonly reported (e.g. age, sex, obesity) exclude others diabetes, cardiovascular disease, hypertension) which might be mediated through measuring general comorbidity numbers medications). identify effects infrequently (psychiatric disorders, infection, dementia aging), how groups correlated useful alternative pre-analysis variable selection. discuss impact limitations joint Bayesian-frequentist inference, mutual insights afforded into long-standing differences statistical scientific discovery.

Язык: Английский

Процитировано

0

Sex hormones and the total testosterone:estradiol ratio as predictors of severe acute respiratory syndrome coronavirus 2 infection in hospitalized men DOI Creative Commons
David Ruiz, Armando Ruiz, María Teresa García‐Unzueta

и другие.

Andrology, Год журнала: 2024, Номер 12(6), С. 1381 - 1388

Опубликована: Янв. 11, 2024

The predictive ability of the early determination sex steroids and total testosterone:estradiol ratio for risk severe coronavirus disease 2019 or potential existence a biological gradient in this relationship has not been evaluated.

Язык: Английский

Процитировано

0

C. Everett Koop Healthcare System for Biosecurity and Defense DOI

Haley R. Warzecha,

Alison Podsednik,

Joseph M. Rosen

и другие.

Springer eBooks, Год журнала: 2024, Номер unknown, С. 165 - 192

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0