Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances
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), t...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/87826 |
| _version_ | 1848764939615862784 |
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| author | Tohira, Hideo Finn, Judith Ball, Stephen Brink, D. Buzzacott, Peter |
| author_facet | Tohira, Hideo Finn, Judith Ball, Stephen Brink, D. Buzzacott, Peter |
| author_sort | Tohira, Hideo |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:27:19Z |
| format | Journal Article |
| id | curtin-20.500.11937-87826 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:27:19Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-878262022-03-04T03:52:08Z Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances Tohira, Hideo Finn, Judith Ball, Stephen Brink, D. Buzzacott, Peter Emergency medical services random forest support vector machine text frequency-inverse document frequency 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. 2021 Journal Article http://hdl.handle.net/20.500.11937/87826 10.1080/17538157.2021.2019038 eng restricted |
| spellingShingle | Emergency medical services random forest support vector machine text frequency-inverse document frequency Tohira, Hideo Finn, Judith Ball, Stephen Brink, D. Buzzacott, Peter Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances |
| title | Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances |
| title_full | Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances |
| title_fullStr | Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances |
| title_full_unstemmed | Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances |
| title_short | Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances |
| title_sort | machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances |
| topic | Emergency medical services random forest support vector machine text frequency-inverse document frequency |
| url | http://hdl.handle.net/20.500.11937/87826 |