ArticleJournal of biomedical informatics2021
Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework.
Article in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Using large language models to identify prediagnostic clinical features of ovarian cancer from healthcare records: a population-based case-control study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
- Joint event extraction model based on dynamic attention matching and graph attention networks.Scientific reports · 2025Article
- CACER: Clinical concept Annotations for Cancer Events and Relations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Annotation of epilepsy clinic letters for natural language processing.Journal of biomedical semantics · 2024Article
- Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation.Heliyon · 2024Article
- Coronavirus Anatomy and Its Analytical Approaches for Targeting COVID-19.Advances in experimental medicine and biology · 2024Review
- Supporting the Diagnosis of Fabry Disease Using a Natural Language Processing-Based Approach.Journal of clinical medicine · 2023Article
- Association of Weight Loss in Ambulatory Care Settings With First Diagnosis of Lung Cancer in the US.JAMA network open · 2023Article
- Article
- Post-Acute COVID-19 Respiratory Symptoms in Patients With Asthma: An Electronic Health Records-Based Study.The journal of allergy and clinical immunology. In practice · 2023Observational
- Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients.BMC medical informatics and decision making · 2023Article
- Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach.BMC medical informatics and decision making · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Generalizing through Forgetting - Domain Generalization for Symptom Event Extraction in Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event.Data science and engineering · 2023Article
- Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.Neural computing & applications · 2023Review
- COVID-19 advising application development for Apple devices (iOS).PeerJ. Computer science · 2023Article
- Article
- The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.Healthcare (Basel, Switzerland) · 2022Review
- Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.Yearbook of medical informatics · 2022Article
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Abstract
Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). Our span-based event extraction model outperforms an extractor built on MetaMapLite for the identification of symptoms with assertion values. In a secondary use application, we predicted COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information, to explore the clinical presentation of COVID-19. Automatically extracted symptoms improve COVID-19 prediction performance, beyond structured data alone.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.