Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances
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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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AL-Smadi, M.; Guetl, Christian (2011)Free-text answers assessment has been a field of interest during the last 50 years. Several free-text answers assessment tools underpinned by different techniques have been developed. In most cases, the complexity of the ...
Lockery, J.E.; Rigby, J.; Collyer, T.A.; Stewart, A.C.; Woods, R.L.; McNeil, J.J.; Reid, Christopher ; Ernst, M.E. (2019)© 2019 Lockery et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the ...
Does Free-Text Information in Falls Incident Reports Assist to Explain How and Why the Falls Occurred in a Hospital Setting?De Jong, Lex; Francis-Coad, J.; Waldron, N.; Ingram, K.; McPhail, S.; Etherton-Beer, C.; Haines, T.; Flicker, L.; Weselman, T.; Hill, A. (2018)OBJECTIVE: The aim of this study was to explore whether information captured in falls reports in incident management systems could be used to explain how and why the falls occurred, with a view to identifying whether such ...