Recognition of medication information from discharge summaries using ensembles of classifiers.
Background: Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine l...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 12; no. 1; pp. 36 - 37 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2012
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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=104387714&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104387714 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 2012 vid: 12 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 104387714 NLM22564405 2011764641 10.1186/1472-6947-12-36 NLM22564405 PMC3502425 104387714 ppf: 36 ppct: 1 formats: tig: atl: Recognition of medication information from discharge summaries using ensembles of classifiers. aug: au: Doan, Son Collier, Nigel Xu, Hua Duy, Pham Hoang Phuong, Tu Minh Pham, Hoang Duy Tu, Minh Phuong affil: National Institute of Informatics, Hitotsubashi, Chiyoda, Tokyo, Japan. sondoan@gmail.com. sug: subj: Information Retrieval Methods Medication Systems Natural Language Processing Patient Discharge Information Science Algorithms Artificial Intelligence Decision Support Techniques Female Human Management Male Drugs Reproducibility of Results Semantics Software Design Female Male ab: Background: Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks.Methods: We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting.Results: Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall) for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall) than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall) by the first-ranked system in the challenge.Conclusions: Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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