National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 1 of 1 Research Studies DisplayedBurgermaster M, Rodriguez VA
Psychosocial-behavioral phenotyping: a novel precision health approach to modeling behavioral, psychological, and social determinants of health using machine learning.
The purpose of this study was to demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, which includes the identification of subgroups with similar combinations of psychosocial characteristics. The researchers conducted a secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883). The study found 20 psychosocial-behavioral phenotypes. Each phenotype suggested different contextual considerations for intervention design. The researchers concluded that psychosocial-behavioral phenotypes can identify possible targets of intervention.
AHRQ-funded; HS019853.
Citation: Burgermaster M, Rodriguez VA .
Psychosocial-behavioral phenotyping: a novel precision health approach to modeling behavioral, psychological, and social determinants of health using machine learning.
Ann Behav Med 2022 Nov 18;56(12):1258-71. doi: 10.1093/abm/kaac012..
Keywords: Social Determinants of Health, Health Information Technology (HIT), Research Methodologies