Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty.

ABSTRACT: Total knee arthroplasty (TKA) is an effective treatment for end stage osteoarthritis. However, biopsychosocial features are not routinely considered in TKA clinical decision-making, despite increasing evidence to support their role in patient recovery. We have developed a more holistic model of patient care by using machine learning and Bayesian inference methods to build patient-centred predictive models, enhanced by a comprehensive battery of biopsychosocial features. Data from 863 patients with TKA (mean age 68 years (SD 8), 50% women), identified between 2019 and 2022 from four hospitals in NSW, Australia, was included in model development. Predictive models for improvement in patient quality-of-life and knee symptomology at three months post-TKA were developed, as measured by a change

SUBMITTER: Ribbons K 

PROVIDER: S-EPMC11811292 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature