About: Predicting individual risk for COVID19 complications using EMR data

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Predicting individual risk for COVID19 complications using EMR data
Creator	Chodick, Gabriel Domany, Tamar Kinar, Yaron Lanyado, Alon Shalev, Varda Shoshan, Avi Yesharim, Rachel
Source	MedRxiv
abstract	Background: The global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is evident but has been hampered by the scarcity of real-world reliable data. Here we describe two approaches: a. the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. a preliminary model that is trained using existing real world COVID-19 data. Methods: We have utilized the computerized data of Maccabi Healthcare Services a 2.3 million member state-mandated health organization in Israel. The age and sex matched matrix used for training the XGBoost ILI-based model included, circa 690,000 rows and 900 features. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n=1658), or hospitalized and marked as mild (n=332), or as having moderate (n=83) or severe (n=64) complications. Findings: The AUC of our models and the priors on the 2137 COVID-19 patients for predicting moderate and severe complications as cases and all other as controls, the AUC for the ILI-based model was 0.852[0.824-0.879] for the COVID19-based model - 0.872[0.847-0.879].. Interpretation: These models can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen.
has issue date	2020-06-05(xsd:dateTime)
bibo:doi	10.1101/2020.06.03.20121574
has license	medrxiv
sha1sum (hex)	d626226916133231b49bbef9fd9c6024a8f522ac
schema:url	https://doi.org/10.1101/2020.06.03.20121574
resource representing a document's title	Predicting individual risk for COVID19 complications using EMR data
resource representing a document's body	covid:d626226916133231b49bbef9fd9c6024a8f522ac#body_text
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