Deep learning prediction of falls among nursing home residents with Alzheimer's disease.

Aim: This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling and multiple complicating factors, including age, dementia severity, lower extremity strength and physical function, among nursing home residents with Alzheimer's disease.

Methods: A total of 42 people with Alzheimer's disease were enrolled. We evaluated falling events from nursing home admission (baseline) to 300 days later. We assessed the knee extension strength and Functional Independence Measure locomotion item and carried out the Mini-Mental State Examination at baseline. To predict falling, participants were categorized into three classes: those who fell within the first 150 (or 300) days from baseline or those who did not experience a fall within the study period. For each class, 1000 bootstrap datasets were generated using 42 actual sample datasets, and were used to propose a CNN algorithm and cross-validate the algorithm.

Results: Eight (19.0%), 11 (26.2%) and 31 participants (73.8%) fell within 150 or 300 days after the baseline assessment or did not fall until 300 days or later, respectively. The highest accuracy rate of the CNN classification was 0.647 in the factor combination extracted from the Mini-Mental State Examination score, knee extension strength and Functional Independence Measure locomotion item score.

Conclusions: A CNN based on multiple complicating factors could predict the time of falling in nursing home residents with Alzheimer's disease. Geriatr Gerontol Int 2020; ••: ••-••.