Using a genetic algorithm to derive a highly predictive and context-specific frailty index.

ABSTRACT: The frailty index (FI) is one of the most widespread tools used to predict poor, health-related outcomes in older persons. The selection of clinical and functional deficits to include in a FI is mostly based on the users' clinical experience. However, this approach may not be sufficiently accurate to predict health outcomes in particular subgroups of individuals. In this study, we implemented an optimization algorithm, the genetic algorithm, to create a highly performant (FI) based on our prediction goals, rather than on a predetermined clinical selection of deficits, using data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) and 109 potential deficits identified in the dataset. The algorithm was personalized to obtain a FI with high discrimination ability

SUBMITTER: Zucchelli A 

PROVIDER: S-EPMC7202492 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature