Predicting future falls in older people using natural language processing of general practitioners’ clinical notes

Abstract

Background Falls in older people are common and morbid. Prediction models can help identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity to develop automated prediction tools that may help to identify fall-prone individuals and lower clinical workload. However, existing models primarily utilise structured EHR data and neglect information in unstructured data. Using machine learning and natural language processing (NLP), we aimed to examine the predictive performance provided by unstructured clinical notes, and their incremental performance over structured data to predict falls. Methods We used primary care EHR data of people aged 65 or over. We developed three logistic regression models using the least absolute shrinkage and selection operator: one using structured clinical variables (Baseline), one with topics extracted from unstructured clinical notes (Topic-based) and one by adding clinical variables to the extracted topics (Combi). Model performance was assessed in terms of discrimination using the area under the receiver operating characteristic curve (AUC), and calibration by calibration plots. We used 10-fold cross-validation to validate the approach. Results Data of 35,357 individuals were analysed, of which 4,734 experienced falls. Our NLP topic modelling technique discovered 151 topics from the unstructured clinical notes. AUCs and 95% confidence intervals of the Baseline, Topic-based and Combi models were 0.709 (0.700–0.719), 0.685 (0.676–0.694) and 0.718 (0.708–0.727), respectively. All the models showed good calibration. Conclusions Unstructured clinical notes are an additional viable data source to develop and improve prediction models for falls compared to traditional prediction models, but the clinical relevance remains limited.

The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review

“…Dormosh et al. [ 17 ] utilized topic extraction and regression analysis to assign odds ratios (OR) to falls risk factors extracted from clinical documents. Seven topics were found with an OR of greater than 1 and conveyed more significant falls risk, with residential care being the most significant (OR 55.69).…”

Topic evolution before fall incidents in new fallers through natural language processing of general practitioners’ clinical notes

“…Our design enabled us to track changes in potential risk factors over an extended period, aligning with the dynamic nature of falls as many risk factors may change over time. The utility of NLP has been previously explored in few studies to identify fall-risk factors [ 19 ] or to predict falls [ 29 , 30 ]. Unlike the studies by [ 19 , 30 ] who examined inpatient falls, our study predominantly focused on community falls and included a representative sample of community-dwelling older individuals.…”

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

“…Machine learning (ML), a subset of artificial intelligence (AI), is a method of self-learning to provide solutions ( 12 , 13 ). According to scholars such as Arthur Samuel, ML provides computers with the ability to learn without explicit programming.…”