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Development of a Post-Acute Sequelae of COVID-19 (PASC) Symptom Lexicon Using Electronic Health Record Clinical Notes
Preprint
em Inglês
| medRxiv
| ID: ppmedrxiv-21261260
ABSTRACT
ObjectiveTo develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon from clinical notes to support PASC symptom identification and research. MethodsWe identified 26,117 COVID-19 positive patients from the Mass General Brighams electronic health records (EHR) and extracted 328,879 clinical notes from their post-acute infection period (day 51-110 from first positive COVID-19 test). The PASC symptom lexicon incorporated Unified Medical Language System(R) (UMLS) Metathesaurus concepts and synonyms based on selected semantic types. The MTERMS natural language processing (NLP) tool was used to automatically extract symptoms from a development dataset. The lexicon was iteratively revised with manual chart review, keyword search, concept consolidation, and evaluation of NLP output. We assessed the comprehensiveness of the lexicon and the NLP performance using a validation dataset and reported the symptom prevalence across the entire corpus. ResultsThe PASC symptom lexicon included 355 symptoms consolidated from 1,520 UMLS concepts. NLP achieved an averaged precision of 0.94 and an estimated recall of 0.84. Symptoms with the highest frequency included pain (43.1%), anxiety (25.8%), depression (24.0%), fatigue (23.4%), joint pain (21.0%), shortness of breath (20.8%), headache (20.0%), nausea and/or vomiting (19.9%), myalgia (19.0%), and gastroesophageal reflux (18.6%). Discussion and ConclusionPASC symptoms are diverse. A comprehensive PASC symptom lexicon can be derived using a data-driven, ontology-driven and NLP-assisted approach. By using unstructured data, this approach may improve identification and analysis of patient symptoms in the EHR, and inform prospective study design, preventative care strategies, and therapeutic interventions for patient care.
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Texto completo:
Disponível
Coleções:
Preprints
Base de dados:
medRxiv
Tipo de estudo:
Cohort_studies
/
Experimental_studies
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Estudo observacional
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Estudo prognóstico
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Pesquisa qualitativa
Idioma:
Inglês
Ano de publicação:
2021
Tipo de documento:
Preprint