ArticleBioengineering (Basel, Switzerland)2025
Predicting Health-Related Quality of Life Using Social Determinants of Health: A Machine Learning Approach with the All of Us Cohort.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Explainable Machine Learning for Risk Prediction of Reduced Quality of Life in Hypertension.Vascular health and risk management · 2026Article
- Longitudinal trajectories of health-related quality of life and their predictors among community-dwelling older adults.Scientific reports · 2025Article
- Review
- Advances in AI Technology in Healthcare.Bioengineering (Basel, Switzerland) · 2025Article
- A machine learning approach to healthcare needs and barriers using the 100% Community Survey of access to SDOH services.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study applied machine learning (ML) algorithms to predict health-related quality of life (HRQOL) using comprehensive social determinants of health (SDOH) features. Data from the All of Us dataset, comprising participants with complete HRQOL and SDOH records, were analyzed. The primary outcome was HRQOL, which encompassed physical and mental health components, while SDOH features included social, educational, economic, environmental, and healthcare access factors. Three ML algorithms, namely logistic regression, XGBoost, and Random Forest, were tested. The models achieved accuracy ranges of 0.73-0.77 for HRQOL, 0.70-0.71 for physical health, and 0.72-0.77 for mental health, with corresponding area under the curve ranges of 0.81-0.84, 0.74-0.76, and 0.83-0.85, respectively. Emotional stability, activity management, spiritual beliefs, and comorbidity were identified as key predictors. These findings underscore the critical role of SDOH in predicting HRQOL and suggests future research to focus on applying such models to diverse patient populations and specific clinical conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.