ArticleJournal of the American Medical Informatics Association : JAMIA
Induced lexico-syntactic patterns improve information extraction from online medical forums.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Identifying Complementary and Alternative Medicine Usage Information from Internet Resources. A Systematic Review.Methods of information in medicine · 2016Pooled it
- Spelling Errors and Shouting Capitalization Lead to Additive Penalties to Trustworthiness of Online Health Information: Randomized Experiment With Laypersons.Journal of medical Internet research · 2020Trial
- Clinical Terminology Mapping Service Based on Information Retrieval.Methods of information in medicine · 2025Article
- Identifying Nonpatient Authors of Patient Portal Secure Messages in Oncology: A Proof-of-Concept Demonstration of Natural Language Processing Methods.JCO clinical cancer informatics · 2022Article
- Pandemic tele-smart: a contactless tele-health system for efficient monitoring of remotely located COVID-19 quarantine wards in India using near-field communication and natural language processing system.Medical & biological engineering & computing · 2022Article
- Parsable Clinical Trial Eligibility Criteria Representation Using Natural Language Processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.BMC bioinformatics · 2019Article
- Beyond opinion classification: Extracting facts, opinions and experiences from health forums.PloS one · 2019Article
- Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review.BMC medical informatics and decision making · 2018Article
- Article
- Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Machine learning to support social media empowered patients in cancer care and cancer treatment decisions.PloS one · 2018Article
- Enhanced Quality Measurement Event Detection: An Application to Physician Reporting.EGEMS (Washington, DC) · 2017Article
- Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews.Journal of healthcare engineering · 2017Article
- Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.Yearbook of medical informatics · 2016Review
- Trends in biomedical informatics: automated topic analysis of JAMIA articles.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Automating the generation of lexical patterns for processing free text in clinical documents.Journal of the American Medical Informatics Association : JAMIA · 2015Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo reliably extract two entity types, symptoms and conditions (SCs), and drugs and treatments (DTs), from patient-authored text (PAT) by learning lexico-syntactic patterns from data annotated with seed dictionaries. BACKGROUND AND SIGNIFICANCE: Despite the increasing quantity of PAT (eg, online discussion threads), tools for identifying medical entities in PAT are limited. When applied to PAT, existing tools either fail to identify specific entity types or perform poorly. Identification of SC and DT terms in PAT would enable exploration of efficacy and side effects for not only pharmaceutical drugs, but also for home remedies and components of daily care. MATERIALS AND
methodsWe use SC and DT term dictionaries compiled from online sources to label several discussion forums from MedHelp (http://www.medhelp.org). We then iteratively induce lexico-syntactic patterns corresponding strongly to each entity type to extract new SC and DT terms.
resultsOur system is able to extract symptom descriptions and treatments absent from our original dictionaries, such as 'LADA', 'stabbing pain', and 'cinnamon pills'. Our system extracts DT terms with 58-70% F1 score and SC terms with 66-76% F1 score on two forums from MedHelp. We show improvements over MetaMap, OBA, a conditional random field-based classifier, and a previous pattern learning approach.
conclusionsOur entity extractor based on lexico-syntactic patterns is a successful and preferable technique for identifying specific entity types in PAT. To the best of our knowledge, this is the first paper to extract SC and DT entities from PAT. We exhibit learning of informal terms often used in PAT but missing from typical dictionaries.
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Registered trials
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.