A machine learning model for frailty based on wearable device measurements

Résumé fourni par la source

Frailty is an important factor in human aging associated with a broad range of adverse outcomes. Frailty metrics are time intensive to collect making them difficult for larger scale application. We apply machine learning to predict these frailty metrics, associated risk factors, and adverse outcomes from activity data. We use activity data collected using Actigraphy wearable accelerometer sensors, which are devices that measure acceleration along three axes of movement. Models were evaluated using Area Under the receiver operator Curve (AUC), Area Under Precision Recall Curve (AUPRC), Spearman rank test, Mann-Whitney U test, or Kruskal-Wallis test on repeated subsampling of train and test sets. All statistical tests are reported using -log10(P-value). Machine learning models show strong predictive performance even with small amounts of accelerometry data available. They are also able to better determine adverse outcomes such as hospitalization and mortality than frailty metrics themselves in our geriatric population. This approach of wearable activity data-based prediction of frailty offers a surrogate (proxy or estimate) for determining frailty metrics in a scalable manner. It can also be used to determine adverse outcomes such as hospitalizations and mortality, allowing frailty to be used as a metric in other studies or medical practices. Frailty occurs during human aging and is associated with a broad range of unfavourable outcomes. Frailty is measured using various scores but these often rely on subjective information, are labor intensive to measure, and are not assessed over time. This work presents objective measures indicative of frailty, based on wearable sensors that measure movement. This was tested in a group of people with an average age of 75. Application of a computational model using this data enabled long-term outcomes, including hospitalization and death, to be more accurately predicted than using existing frailty measures. This work demonstrates that 48 hours of data collection per patient is sufficient. This type of system could be used on a larger number of people, enabling those at risk of unfavourable outcomes to be targeted with medical interventions or support. Culos, Manas et al. construct machine learning surrogate frailty metrics from activity data collected in a nominally intrusive manner. They show that a limited amount of activity data is necessary to model frailty metrics allowing for an increased proliferation of their application along with a robust source of data for other age-related outcomes.

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