About: Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app Goto Sponge NotDistinct Permalink

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
title	Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app
Creator	Cardoso, M Nguyen, H Franks, Paul Wolf, Jonathan Chan, Andrew »more»
source	MedRxiv
abstract	As no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between May 1- May 28th, 2020. Using the first 5 days of symptom logging, the ROC-AUC of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.
has issue date	2020-06-16(xsd:dateTime)
bibo:doi	10.1101/2020.06.12.20129056
has license	medrxiv
sha1sum (hex)	7557d933870b23b5c5647f40975ce07a75ab3515
schema:url	https://doi.org/10.1101/2020.06.12.20129056
resource representing a document's title	Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app
resource representing a document's body	covid:7557d933870b23b5c5647f40975ce07a75ab3515#body_text
is schema:about of	named entity 'prediction' named entity 'tool' named entity 'medical support' named entity 'medRxiv' named entity 'preprint' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software