ArticleJournal of biomedical informatics2022
PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes.
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Immunological Mechanisms and Machine Learning Applications in Post-COVID-19 Syndrome: A Narrative Review.Microorganisms · 2026Article
- A retrospective cohort study of viral and sociodemographic determinants of long COVID among Idaho veterans.Frontiers in public health · 2026Article
- A Mixed Methods Analysis of Long COVID Symptoms in Black Americans: Examining Physical and Mental Health Outcomes.Journal of racial and ethnic health disparities · 2025Article
- A natural language processing pipeline for identifying pediatric long COVID symptoms and functional impacts in freeform clinical notes: a RECOVER study.JAMIA open · 2025Article
- Extracting post-acute sequelae of SARS-CoV-2 infection symptoms from clinical notes via hybrid natural language processing.npj health systems · 2025Article
- Label efficient phenotyping for Long COVID using electronic health records.NPJ digital medicine · 2025Article
- Acute serum protein biomarker profile and prevalence of persistent (>6 months) neuropsychiatric symptoms in a cohort of SARS-CoV-2 PCR positive patients in Cape Town, South Africa.Brain, behavior, & immunity - health · 2025Article
- Association of glycemic control with Long COVID in patients with type 2 diabetes: findings from the National COVID Cohort Collaborative (N3C).BMJ open diabetes research & care · 2025Article
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- Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative.JAMIA open · 2024Article
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- Beyond the acute: pain in long COVID survivors at 1.5 years.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Knowledge Representation and Management in the Age of Long Covid and Large Language Models: a 2022-2023 Survey.Yearbook of medical informatics · 2024Review
- Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.Journal of biomedical informatics · 2024Article
- Streamlining social media information retrieval for public health research with deep learning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study.EClinicalMedicine · 2024Article
- Association between SARS-CoV-2 infection and select symptoms and conditions 31 to 150 days after testing among children and adults.BMC infectious diseases · 2024Article
- Potential Predictors of Long COVID in Italian Children: A Cross-Sectional Survey.Children (Basel, Switzerland) · 2024Article
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6 authors.
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Abstract
objectiveTo develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon (PASCLex) from clinical notes to support PASC symptom identification and research.
methodsWe identified 26,117 COVID-19 positive patients from the Mass General Brigham's 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). PASCLex incorporated Unified Medical Language System® (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 PASCLex and the NLP performance using a validation dataset and reported the symptom prevalence across the entire corpus.
resultsPASCLex included 355 symptoms consolidated from 1520 UMLS concepts of 16,466 synonyms. 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 lexicon of PASC symptoms can be derived using an ontology-driven, EHR-guided 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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