ArticleNature communications2024
A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low-Income Populations Within United States Health Systems: A Scoping Review.Health science reports · 2026Article
- Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction.JAMIA open · 2026Article
- Co-designing an Outpatient Clinical Decision Support Prototype for Managing Social Risks in Patients Living with Dementia.Applied clinical informatics · 2026Article
- Article
- The forgotten - overcoming challenges in diabetes care for marginalized populations.Expert review of endocrinology & metabolism · 2025Review
- Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists.Clinical epidemiology · 2025Review
- Article
- Standardizing social determinants of health data: a proposal for a comprehensive screening tool to address health equity a systematic review.Health affairs scholar · 2024Article
- Article
- Social Environment, Lifestyle, and Genetic Predisposition With Dementia Risk: A Long-Term Longitudinal Study Among Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- Integrating the Polysocial Risk Score: Enhancing Comprehensive Healthcare Delivery.Methodist DeBakey cardiovascular journal · 2024Review
- Toward Integrating Machine Learning-powered Polysocial Risk Scores into Electronic Health Record Workflows.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
- Update of
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Racial and ethnic minorities bear a disproportionate burden of type 2 diabetes (T2D) and its complications, with social determinants of health (SDoH) recognized as key drivers of these disparities. Implementing efficient and effective social needs management strategies is crucial. We propose a machine learning analytic pipeline to calculate the individualized polysocial risk score (iPsRS), which can identify T2D patients at high social risk for hospitalization, incorporating explainable AI techniques and algorithmic fairness optimization. We use electronic health records (EHR) data from T2D patients in the University of Florida Health Integrated Data Repository, incorporating both contextual SDoH (e.g., neighborhood deprivation) and person-level SDoH (e.g., housing instability). After fairness optimization across racial and ethnic groups, the iPsRS achieved a C statistic of 0.71 in predicting 1-year hospitalization. Our iPsRS can fairly and accurately screen patients with T2D who are at increased social risk for hospitalization.
Indexed as
Identifiers
What Socratic holds
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.