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

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.

KEYWORDS:

• Emergency medical services
• random forest
• support vector machine
• text frequency-inverse document frequency

Availability of Data and Material

Data used in this study are not available because the data contain the personal information of participants.

Authorship contribution statement

HT conceived the study, analyzed data, built models, and derived the manuscript. PB, SB, and JF provided critical feedback contributed to and approved the final manuscript.

Code availability

The code used in this study is not available because it contains the personal information of participants.

Ethics approval

This study was approved by the Curtin University Human Research Ethics Committee (Reference number: HR128/2013).

Disclosure statement

DB is an employee of St John Western Australia (SJWA); SB and JF have an adjunct research position with SJWA; and JF receives research funding from SJWA.

Additional information

Funding

This project was, in part, funded by the Curtin University 2018 School of Nursing Midwifery and Paramedicine Research Grant.