ArticleIEEE journal of biomedical and health informatics2022
A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media.
Article in IEEE journal of biomedical and health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Leveraging large language models on free-text symptoms from participatory surveillance enhances pertussis forecasting in the United States.BMC infectious diseases · 2026Article
- Scalable tracking of symptoms in the electronic health record using large language models in patients with central nervous system cancers undergoing therapy.Neuro-oncology · 2026Article
- Causal networks guiding large language models: application to COVID-19.Health care management science · 2025Article
- Article
- From Conversation to Standardized Terminology: An LLM-RAG Approach for Automated Health Problem Identification in Home Healthcare.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Extracting Symptoms of Complex Conditions From Online Discourse (Subreddit to Symptomatology): Lexicon-Based Approach.JMIR medical informatics · 2025Article
- Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: a Comparative Study of Four AI-Powered Engines.Journal of healthcare informatics research · 2025Article
- Multilingual Virtual Healthcare Assistant.Health care science · 2025Article
- Symptoms of Asthma Extracted Through Natural Language Processing and Their Associations With Acute Asthma Exacerbation in Adults With Mild Asthma.The journal of allergy and clinical immunology. In practice · 2025Article
- Article
- Article
- Capturing Patients' Lived Experiences with Chronic Pain through Motivational Interviewing and Information Extraction.Proceedings of the ... Workshop on Patient-Oriented Language Processing · 2025Article
- Optimized BERT-based NLP outperforms zero-shot methods for automated symptom detection in clinical practice.Frontiers in digital health · 2025Article
- Zero-shot learning to extract assessment criteria and medical services from the preventive healthcare guidelines using large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Identifying symptom etiologies using syntactic patterns and large language models.Scientific reports · 2024Article
- Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning.Frontiers in digital health · 2024Article
- Identifying COVID-19 cases and extracting patient reported symptoms from Reddit using natural language processing.Scientific reports · 2023Article
- Forecasting User Interests Through Topic Tag Predictions in Online Health Communities.IEEE journal of biomedical and health informatics · 2023Article
- Constructing a disease database and using natural language processing to capture and standardize free text clinical information.Scientific reports · 2023Article
- Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study.Journal of medical Internet research · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Patients experience various symptoms when they haveeither acute or chronic diseases or undergo some treatments for diseases. Symptoms are often indicators of the severity of the disease and the need for hospitalization. Symptoms are often described in free text written as clinical notes in the Electronic Health Records (EHR) and are not integrated with other clinical factors for disease prediction and healthcare outcome management. In this research, we propose a novel deep language model to extract patient-reported symptoms from clinical text. The deep language model integrates syntactic and semantic analysis for symptom extraction and identifies the actual symptoms reported by patients and conditional or negation symptoms. The deep language model can extract both complex and straightforward symptom expressions. We used a real-world clinical notes dataset to evaluate our model and demonstrated that our model achieves superior performance compared to three other state-of-the-art symptom extraction models. We extensively analyzed our model to illustrate its effectiveness by examining each component's contribution to the model. Finally, we applied our model on a COVID-19 tweets data set to extract COVID-19 symptoms. The results show that our model can identify all the symptoms suggested by the Center for Disease Control (CDC) ahead of their timeline and many rare symptoms.
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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.