Combination of conditional random field with a rule based method in the extraction of PICO elements.
Background: Extracting primary care information in terms of Patient/Problem, Intervention, Comparison and Outcome, known as PICO elements, is difficult as the volume of medical information expands and the health semantics is complex to capture it from unstructured information. The combination of the...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 18; no. 1 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
12/4/2018
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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=133368423&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133368423 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 12/4/2018 vid: 18 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 133368423 133368423 NLM30509272 10.1186/s12911-018-0699-2 NLM30509272 133368423 ppct: 1 formats: tig: atl: Combination of conditional random field with a rule based method in the extraction of PICO elements. aug: au: Chabou, Samir Iglewski, Michal affil: Computer Science and Engineering Department, Université du Québec en Outaouais, J8Y 3G5, Gatineau, Canada sug: subj: Medical Informatics Models, Statistical Natural Language Processing Data Mining Ferrans and Powers Quality of Life Index Scales ab: Background: Extracting primary care information in terms of Patient/Problem, Intervention, Comparison and Outcome, known as PICO elements, is difficult as the volume of medical information expands and the health semantics is complex to capture it from unstructured information. The combination of the machine learning methods (MLMs) with rule based methods (RBMs) could facilitate and improve the PICO extraction. This paper studies the PICO elements extraction methods. The goal is to combine the MLMs with the RBMs to extract PICO elements in medical papers to facilitate answering clinical questions formulated with the PICO framework.Methods: First, we analyze the aspects of the MLM model that influence the quality of the PICO elements extraction. Secondly, we combine the MLM approach with the RBMs in order to improve the PICO elements retrieval process. To conduct our experiments, we use a corpus of 1000 abstracts.Results: We obtain an F-score of 80% for P element, 64% for the I element and 92% for the O element. Given the nature of the used training corpus where P and I elements represent respectively only 6.5 and 5.8% of total sentences, the results are competitive with previously published ones.Conclusions: Our study of the PICO element extraction shows that the task is very challenging. The MLMs tend to have an acceptable precision rate but they have a low recall rate when the corpus is not representative. The RBMs backed up the MLMs to increase the recall rate and consequently the combination of the two methods gave better results. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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