Evaluating resampling methods and structured features to improve fall incident report identification by the severity level.
Objective: This study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.Materials and Methods: We present an incident report classification (IRC) framework to classify the in-hospital fall inci...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 28; no. 8; pp. 1756 - 1765 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Aug2021
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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=151699288&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151699288 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: Aug2021 vid: 28 iid: 8 pid: 622 pub: Oxford University Press / USA artinfo: ui: 151699288 151699288 NLM34010385 151699288 10.1093/jamia/ocab048 NLM34010385 151699288 ppf: 1756 ppct: 9 formats: tig: atl: Evaluating resampling methods and structured features to improve fall incident report identification by the severity level. aug: au: Liu, Jiaxing Wong, Zoie S Y So, H Y Tsui, Kwok Leung affil: School of Statistics and Mathematics, Zhongnan University of Economics and Law , Wuhan, China sug: subj: Risk Management Algorithms Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Arthritis Impact Measurement Scales Questionnaires ab: Objective: This study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.Materials and Methods: We present an incident report classification (IRC) framework to classify the in-hospital fall incident severity level by addressing the imbalanced class problem and incorporating structured attributes. After text preprocessing, bag-of-words features, structured text features, and structured clinical features were extracted from the reports. Next, resampling techniques were incorporated into the training process. Machine learning algorithms were used to build classification models. IRC systems were trained, validated, and tested using a repeated and randomly stratified shuffle-split cross-validation method. Finally, we evaluated the system performance using the F1-measure, precision, and recall over 15 stratified test sets.Results: The experimental results demonstrated that the classification system setting considering both data imbalance issues and structured features outperformed the other system settings (with a mean macro-averaged F1-measure of 0.733). Considering the structured features and resampling techniques, this classification system setting significantly improved the mean F1-measure for the rare class by 30.88% (P value < .001) and the mean macro-averaged F1-measure by 8.26% from the baseline system setting (P value < .001). In general, the classification system employing the random forest algorithm and random oversampling method outperformed the others.Conclusions: Structured features provide essential information for categorizing the fall incident severity level. Resampling methods help rebalance the class distribution of the original incident report data, which improves the performance of machine learning models. The IRC framework presented in this study effectively automates the identification of fall incident reports by the severity level. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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