Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia.
Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these record...
| Publicado en: | Studies in Health Technology & Informatics Vol. 310; pp. 695 - 700 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
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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=175248864&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175248864 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 310 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 175248864 175248864 175248864 10.3233/SHTI231054 175248864 ppf: 695 ppct: 5 formats: tig: atl: Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia. aug: au: CHATURVEDI, Jaya VELUPILLAI, Sumithra STEWART, Robert ROBERTS, Angus affil: Institute of Psychiatry, Psychology and Neurosciences, King's College London sug: subj: Pain Natural Language Processing Mental Health Electronic Health Records Medical Informatics Congresses and Conferences New South Wales New South Wales Human Resource Databases Machine Learning Algorithms Descriptive Statistics Comparative Studies Confidence Intervals kappa Statistic ab: Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. A machine learning based classification algorithm is trained to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train four classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99). pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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