ArticleSSM - population health2018
Machine learning approaches to the social determinants of health in the health and retirement study.
Article in SSM - population health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study.JMIR formative research · 2024Article
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- Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study.World journal of gastrointestinal surgery · 2024Article
- Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019.BMC medicine · 2023Article
- Creating Patient Context: Empathy and Attitudes Toward Diabetes Following Virtual Immersion.Journal of diabetes science and technology · 2023Article
- Inequities in Mental Health Care Facing Racialized Immigrant Older Adults With Mental Disorders Despite Universal Coverage: A Population-Based Study in Canada.The journals of gerontology. Series B, Psychological sciences and social sciences · 2023Article
- Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures.Journal of orthopaedic surgery and research · 2023Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
- Using machine learning to understand determinants of IUD use in India: Analyses of the National Family Health Surveys (NFHS-4).SSM - population health · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
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- Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting.PloS one · 2022Article
- Use of machine learning to examine disparities in completion of substance use disorder treatment.PloS one · 2022Article
- Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning.Epidemiology (Cambridge, Mass.) · 2021Article
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3 authors.
Funding
Abstract
backgroundSocial and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study.
methodsA linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks.
resultsAll models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( DISCUSSION: Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.
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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 OpenQuestion graph.