ReviewScience advances2022
Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.
Review in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Healthy aging meta-analyses and scoping review of risk factors across Latin America reveal large heterogeneity and weak predictive models.Nature aging · 2024Pooled it
- Using Machine Learning Methodology to Identify Predictors of Non-Response to MTSS-B: a Focus on Discipline Problems and Juvenile Justice Involvement.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Theory and practice in biomedical informatics: a framework for discovery.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Development and validation of an interpretable machine learning model using routine laboratory biomarkers to stratify severe pneumonia risk in young children.Journal of advanced research · 2026Article
- Toward causal artificial intelligence for biologic treatment response in rheumatoid arthritis: current evidence and future directions.Rheumatology international · 2026Review
- Development of an interpretable machine learning model for predicting stress urinary incontinence following the delivery of a first child.BMC pregnancy and childbirth · 2026Article
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Extreme heat exacerbates atmospheric ozone pollution: Holistic data-based causal investigation in China.iScience · 2026Article
- Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention.Lancet regional health. Americas · 2026Review
- Psychopathology profiles and longitudinal correlates of nonsuicidal self-injury in youth: a machine-learning approach.Translational psychiatry · 2026Article
- Improving accuracy in the estimation of probable dementia in racially and ethnically diverse groups with penalized regression and transfer learning.American journal of epidemiology · 2026Article
- Actigraphy and subjective sleep predictors of nine-year generalized anxiety disorder.Journal of anxiety disorders · 2026Article
- A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations.Frontiers in psychiatry · 2026Article
- Methodological innovations in studying marginalized populations in research on neurodegeneration and ageing.Frontiers in aging neuroscience · 2026Review
- Article
- Mining the gaps: Deciphering Alzheimer's biology through AI-driven reconciliation.The journal of prevention of Alzheimer's disease · 2025Article
- Uncovering the Role of Neuropsychiatric Symptoms in Cognitive Impairment Progression.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Multivariable machine learning prediction of risky alcohol use in contemporary youth.Addiction (Abingdon, England) · 2025Article
- Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.Current environmental health reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.
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.