Corpus overview


Overview

MeSH Disease

Human Phenotype

Fever (1)

Cough (1)

Hypertension (1)


Transmission

Seroprevalence
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    Ruling In and Ruling Out COVID-19: Computing SARS-CoV-2 Infection Risk TRANS Infection Risk TRANS From Symptoms, Imaging and Test Data.

    Authors: Chistopher D'Ambrosia; Henrik Christensen; Eliah Aronoff-Spencer

    doi:10.1101/2020.09.18.20197582 Date: 2020-09-22 Source: medRxiv

    Background: Assigning meaningful probabilities of SARS CoV2 infection risk TRANS infection risk TRANS presents a diagnostic challenge across the continuum of care. Methods: We integrated patient symptom and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS CoV 2 infection MESHD. We trained models with 100,000 simulated patient profiles based on thirteen symptoms, estimated local prevalence SERO, imaging, and molecular diagnostic performance SERO from published reports. We tested these models with consecutive patients who presented with a COVID 19 compatible illness at the University of California San Diego Medical Center over 14 days starting in March 2020. Results: We included 55 consecutive patients with fever HP fever MESHD (78%) or cough HP cough MESHD (77%) presenting for ambulatory (n=11) or hospital care (n=44). 51% (n=28) were female TRANS, 49% were age TRANS <60. Common comorbidities included diabetes MESHD (22%), hypertension HP hypertension MESHD (27%), cancer MESHD (16%) and cardiovascular disease MESHD (13%). 69% of these (n=38) were RT-PCR confirmed positive for SARS CoV2 infection, 11 had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS CoV2 infection MESHD and alternate diagnoses with sensitivities SERO of 81.6 to 84.2%, specificities of 58.8 to 70.6%, and accuracies of 61.4 to 71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices. Conclusions: Decision support models that incorporate symptoms and available test results can help providers diagnose SARS CoV2 infection MESHD in real world settings.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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