Neural negated entity recognition in Spanish electronic health records.
This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated events (detection of absence of some events) help ascertain current medical conditions. For artificial intelligence, negation detection is a valuable point...
| Publicado en: | Journal of Biomedical Informatics Vol. 105 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
May2020
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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=143235056&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143235056 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: May2020 vid: 105 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 143235056 143235056 NLM32298847 143235056 10.1016/j.jbi.2020.103419 NLM32298847 143235056 ppct: 1 formats: tig: atl: Neural negated entity recognition in Spanish electronic health records. aug: au: Santiso, Sara Pérez, Alicia Casillas, Arantza Oronoz, Maite affil: IXA Group, University of the Basque Country (UPV-EHU), ManuelLardizabal 1, 20080 Donostia, Spain sug: subj: Natural Language Processing Artificial Intelligence Comparative Studies Multicenter Studies Evaluation Research Validation Studies Human ab: This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated events (detection of absence of some events) help ascertain current medical conditions. For artificial intelligence, negation detection is a valuable point as it can revert the meaning of a part of a text and, accordingly, influence other tasks such as medical dosage adjustment, the detection of adverse drug reactions or hospital acquired diseases. We focus on negated medical events such as disorders, findings and allergies. From Natural Language Processing (NLP) background, we refer to them as negated medical entities. A novelty of this work is that we approached this task as Named Entity Recognition (NER) with the restriction that just negated medical entities must be recognized (in an attempt to help distinguish them from non-negated ones). Our study is driven with Electronic Health Records (EHRs) written in Spanish. A challenge to cope with is the lexical variability (alternative medical forms, abbreviations, etc.). To this end, we employed an approach based on deep learning. Specifically, the system combines character embeddings to cope with out-of-vocabulary (OOV) words, Long Short-Term Memory (LSTM) networks to model contextual representations and it makes use of Conditional Random Fields (CRF) to classify each medical entity as either negated or not given the contextual dense representation. Moreover, we explored both embeddings created from words and embeddings created from lemmas. The best results were obtained with the lemmatized embeddings. Apparently, this approach reinforced the capability of the LSTMs to cope with the high lexical variability. The f-measure for exact-match was 65.1 and 82.4 for the partial-match. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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