About: Artificial Intelligence for COVID-19 Risk Classification in Kidney Disease: Can Technology Unmask an Unseen Disease?

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Artificial Intelligence for COVID-19 Risk Classification in Kidney Disease: Can Technology Unmask an Unseen Disease?
Creator	Kotanko, Peter Usvyat, Len Larkin, John Belmonte, Kathleen Bermudez, Kristine Chaudhuri, Sheetal Dahne-Steuber, Ines Han, Hao Hymes, Jeffrey Jiao, Yue Kooman, Jeroen Kossmann, Robert Maddux, Franklin Monaghan, Caitlin Neri, Luca Weinhandl, Eric
Source	MedRxiv
abstract	Background: We developed two unique machine learning (ML) models that predict risk of: 1) a major COVID-19 outbreak in the service county of a local HD population within following week, and 2) a hemodialysis (HD) patient having an undetected SARS-CoV-2 infection that is identified after following 3 or more days. Methods: We used county-level data from United States population (March 2020) and HD patient data from a network of clinics (February-May 2020) to develop two ML models. First was a county-level model that used data from general and HD populations (21 variables); outcome of a COVID-19 outbreak in a dialysis service area was defined as a clinic being located in one of the national counties with the highest growth in COVID-19 positive cases (number and people per million (ppm)) in general population during 22-28 Mar 2020. Second was a patient-level model that used HD patient data (82 variables) to predict an individual having an undetected SARS-CoV-2 infection that is identified in subsequent [≥]3 days. Results: Among 1682 counties with dialysis clinics, 82 (4.9%) had a COVID-19 outbreak during 22-28 Mar 2020. Area under the receiver operating characteristic curve (AUROC) for the county-level model was 0.86 in testing dataset. Top predictor of a county experiencing an outbreak was the COVID-19 positive ppm in the general population in the prior week. In a select group (n=11,664) used to build the patient-level model, 28% of patients had COVID-19; prevalence was by design 10% in the testing dataset. AUROC for the patient-level model was 0.71 in the testing dataset. Top predictor of an HD patient having a SARS-CoV-2 infection was mean pre-HD body temperature in the prior week. Conclusions: Developed ML models appear suitable for predicting counties at risk of a COVID-19 outbreak and HD patients at risk of having an undetected SARS-CoV-2 infection.
has issue date	2020-06-17(xsd:dateTime)
bibo:doi	10.1101/2020.06.15.20131680
has license	medrxiv
sha1sum (hex)	22339fbf6d272c9a16ba817569ca4fe3ea38c35f
schema:url	https://doi.org/10.1101/2020.06.15.20131680
resource representing a document's title	Artificial Intelligence for COVID-19 Risk Classification in Kidney Disease: Can Technology Unmask an Unseen Disease?
resource representing a document's body	covid:22339fbf6d272c9a16ba817569ca4fe3ea38c35f#body_text
is schema:about of	named entity 'Disease' named entity 'COUNT' named entity 'Title' named entity 'Kidney Disease' named entity 'COVID-19' named entity 'medRxiv' named entity 'preprint' named entity 'dialysis' named entity 'SHAP' named entity 'Hispanic' named entity 'AUROC' named entity 'CC-BY-ND' named entity 'dialysis' named entity 'medRxiv' named entity 'preprint' named entity 'preprint' named entity 'COVID' named entity 'medRxiv' named entity 'kidney disease' named entity 'positive or negative' named entity 'preprint' named entity 'COVID' named entity 'China' named entity 'United States' named entity 'medRxiv' named entity 'infection' named entity 'infection' named entity 'comorbidities' named entity 'COVID' named entity 'preprint' named entity 'Delaware' named entity 'SARS-CoV-2' named entity 'fever' named entity 'dialysis' named entity 'general population' named entity 'false positives' named entity 'COVID' named entity 'COVID-19' named entity 'albumin' named entity 'infection' named entity 'ferritin' named entity 'COVID' named entity 'COVID-19 testing' named entity 'United States county' named entity 'preprint' named entity 'COVID-19 outbreak' named entity 'peer review' named entity 'preprint' named entity 'COVID' named entity 'SARS-CoV-2' named entity 'COVID' named entity 'dialysis' named entity 'medRxiv' named entity 'preprint' named entity 'COVID-19' named entity 'asymptomatic patients' named entity 'fever' named entity 'New York City' named entity 'medRxiv' named entity '98.6°F' named entity 'COVID-19 outbreak' named entity 'COVID-19' named entity 'COVID-19 test' named entity 'United States' named entity 'COVID-19 outbreak' named entity 'signs and symptoms' named entity 'Kidney' named entity 'rRT-PCR' named entity 'dialysis' named entity 'preprint'

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