Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods.
Objective: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developing a computer system that can assist in incorporati...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 27; no. 1; pp. 81 - 89 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2020
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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=141218440&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141218440 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: Jan2020 vid: 27 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 141218440 141218440 NLM31605490 141218440 10.1093/jamia/ocz150 NLM31605490 141218440 ppf: 81 ppct: 8 formats: tig: atl: Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods. aug: au: Moen, Hans Hakala, Kai Peltonen, Laura-Maria Suhonen, Henry Ginter, Filip Salakoski, Tapio Salanterä, Sanna affil: Department of Future Technologies, University of Turku, Turku, Finland sug: subj: Nursing Records Natural Language Processing Subject Headings Finland Comparative Studies Multicenter Studies Evaluation Research Validation Studies Short Portable Mental Status Questionnaire Scales ab: Objective: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developing a computer system that can assist in incorporating suitable subject headings that reflect the documented topics. Central in this study is performance evaluation of several text classification methods to assess the feasibility of developing such a system.Materials and Methods: Seven text classification methods are evaluated using a corpus of approximately 0.5 million nursing notes (5.5 million sentences) with 676 unique headings extracted from a Finnish university hospital. Several of these methods are based on artificial neural networks. Evaluation is first done in an automatic manner for all methods, then a manual error analysis is done on a sample.Results: We find that a method based on a bidirectional long short-term memory network performs best with an average recall of 0.5435 when allowed to suggest 1 subject heading per sentence and 0.8954 when allowed to suggest 10 subject headings per sentence. However, other methods achieve comparable results. The manual analysis indicates that the predictions are better than what the automatic evaluation suggests.Conclusions: The results indicate that several of the tested methods perform well in suggesting the most appropriate subject headings on sentence level. Thus, we find it feasible to develop a text classification system that can support the use of standardized terminologies and save nurses time and effort on care documentation. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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