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

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has 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 Solomon, Hugo Soundararajan, Venky Venkatakrishnan, A Wagner, Tyler Awasthi, Samir Badley, Andrew Halamka, John Anand, Praveen Challener, Douglas Doctor, Zainab Gores, Gregory Kang, Martin Liebers, Matt Mahmood, Maryam Murugadoss, Karthik Pickering, Brian Rizza, Stacey Shweta, Fnu Silvert, Eli Vergidis, Paschalis Williams, Amy Wilson, Walter
Source	ArXiv
abstract	Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive fine-grained resolution of pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 15.8 million clinical notes from 30,494 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=635) versus COVID-19-negative (COVIDneg, n=29,859) patients over each day of the week preceding the PCR testing date, we identify anosmia/dysgeusia (37.4-fold), myalgia/arthralgia (2.6-fold), diarrhea (2.2-fold), fever/chills (2.1-fold), respiratory difficulty (1.9-fold), and cough (1.8-fold) as significantly amplified in COVIDpos over COVIDneg patients. The specific combination of cough and diarrhea has a 3.2-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 retraining underlying neural networks, thus promising EHR-powered early diagnosis for a broad spectrum of diseases.
has issue date	2020-04-17(xsd:dateTime)
has license	arxiv
sha1sum (hex)	d9be5e947819328ac6bc7b228034624f5a63b300
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:d9be5e947819328ac6bc7b228034624f5a63b300#body_text
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