Induced lexico-syntactic patterns improve information extraction from online medical forums.
Objective: To 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, o...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 21; no. 5; pp. 902 - 910 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103985580&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103985580 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Sep2014 vid: 21 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 103985580 NLM24970840 2012683778 10.1136/amiajnl-2014-002669 NLM24970840 PMC4147618 103985580 ppf: 902 ppct: 8 formats: tig: atl: Induced lexico-syntactic patterns improve information extraction from online medical forums. aug: au: Gupta, Sonal MacLean, Diana L Heer, Jeffrey Manning, Christopher D affil: Department of Computer Science, Stanford University, Stanford, California, USA. sug: subj: Consumer Health Information Data Mining Methods Internet Natural Language Processing Diagnosis Reference Books Disease Drug Therapy Medical Records, Personal Human Linguistics ab: Objective: To 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 Methods: We 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.Results: Our 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.Conclusions: Our 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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