About: Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis

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About: Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis Goto Sponge NotDistinct Permalink

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis
Creator	Temesgen, Zelalem Bauer, Philippe O'horo, John Puranik, Arjun Razonable, Raymund »more»
source	MedRxiv
abstract	Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive fine-grained resolution of the pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 8.2 million clinical notes from 14,967 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=272) versus COVID-19-negative (COVIDneg, n=14,695) patients over each day of the week preceding the PCR testing date, we identify diarrhea (2.8-fold), change in appetite (2-fold), anosmia/dysgeusia (28.6-fold), and respiratory failure (2.1-fold) as significantly amplified in COVIDpos over COVIDneg patients. The specific combination of cough and diarrhea has a 4-fold amplification in COVIDpos patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19 (4-7 days prior to typical PCR testing date). This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional knowledge captured in EHRs. The platform holds tremendous potential for scaling up curation throughput, with minimal need for training underlying neural networks, thus promising EHR-powered early diagnosis for a broad spectrum of diseases.
has issue date	2020-04-23(xsd:dateTime)
bibo:doi	10.1101/2020.04.19.20067660
has license	medrxiv
sha1sum (hex)	acd627554e914e5755e7d4920f36115d80c1a874
schema:url	https://doi.org/10.1101/2020.04.19.20067660
resource representing a document's title	Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis
resource representing a document's body	covid:acd627554e914e5755e7d4920f36115d80c1a874#body_text
is schema:about of	named entity 'curation' named entity 'prior' named entity 'contrasting' named entity 'amplified' named entity 'identify' »more»

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