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dc.contributor.authorTohira, Hideo
dc.contributor.authorFinn, Judith
dc.contributor.authorBall, Stephen
dc.contributor.authorBrink, D.
dc.contributor.authorBuzzacott, Peter
dc.identifier.citationTohira, H. and Finn, J. and Ball, S. and Brink, D. and Buzzacott, P. 2021. Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances. Informatics for Health and Social Care.

We derived machine learning models utilizing features generated by natural language processing (NLP) of free-text data from an ambulance services provider to identify fall cases. The data comprised samples of electronic patient care records care records (ePCRs) from St John Western Australia (WA), the sole ambulance services provider in most of WA. We manually labeled fall cases by reviewing the free-text summary. The models used features including case characteristics (e.g., age) and text frequency-inverse document frequency (tf-idf) of each word of the free-text generated by NLP. Support vector machine (SVM) and random forest were used as classifiers. We compared the performance of the models against the manual identification of falls by recall, precision, and F-measure. A total of 9,447 cases (1%) were randomly sampled, of which 1,648 (17%) were labeled as fall. The best model was an SVM model using case characteristics and tf-idf’s of the first 100 words of free-text, with recall of 0.84, precision of 0.86, and F-measure of 0.85. This performance was better than an SVM model with only case characteristics. Machine-learning models incorporated with features generated by NLP improved the performance of classifying fall cases compared with models without such features. Scope remains for further improvement.

dc.subjectEmergency medical services
dc.subjectrandom forest
dc.subjectsupport vector machine
dc.subjecttext frequency-inverse document frequency
dc.titleMachine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances
dc.typeJournal Article
dcterms.source.titleInformatics for Health and Social Care
curtin.departmentCurtin School of Nursing
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Health Sciences
curtin.contributor.orcidBall, Stephen [0000-0002-9457-3381]
curtin.contributor.orcidTohira, Hideo [0000-0002-2244-8004]
curtin.contributor.orcidFinn, Judith [0000-0002-7307-7944]
curtin.contributor.orcidBuzzacott, Peter [0000-0002-5926-1374]
curtin.contributor.researcheridTohira, Hideo [E-5431-2012]
curtin.contributor.researcheridFinn, Judith [B-2678-2010]
curtin.contributor.scopusauthoridBall, Stephen [55676853700]
curtin.contributor.scopusauthoridTohira, Hideo [6506836786]
curtin.contributor.scopusauthoridFinn, Judith [57200768752] [7202432925]
curtin.contributor.scopusauthoridBuzzacott, Peter [6506509899]

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