Background Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing Electronic Heath Records (EHR) provide opportunities but up to now showed limited clinical value as risk stratification tool; because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHR and to internally validate its predictive performance. Methods EHR data of individuals aged 65 or over. Age, sex, history of falls, medications and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration. Results Data of 36,470 eligible participants were extracted from the dataset. The number of participants who fell at least once was 4,778 (13.1%). The final prediction model included age, sex, history of falls, two medications and five medical conditions. The model had a median area under the receiver operating curve of 0.705 (IQR 0.700-0.714) . Conclusions Our prediction model to identify older people at high risk for falls achieved fair discrimination, and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests.

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.

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