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    Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances

    Access Status
    Fulltext not available
    Authors
    Tohira, Hideo
    Finn, Judith
    Ball, Stephen
    Brink, D.
    Buzzacott, Peter
    Date
    2021
    Type
    Journal Article
    
    Metadata
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    Citation
    Tohira, 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.
    Source Title
    Informatics for Health and Social Care
    DOI
    10.1080/17538157.2021.2019038
    ISSN
    1753-8157
    Faculty
    Faculty of Health Sciences
    School
    Curtin School of Nursing
    URI
    http://hdl.handle.net/20.500.11937/87826
    Collection
    • Curtin Research Publications
    Abstract

    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.

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