What this solves
HRS-scale datasets are rich but difficult to operationalize. Health DKM resolves fragmented signal into interpretable state models and leverage points.
Project Spotlight
Health DKM on HRS Data
Health DKM turns complex longitudinal Health and Retirement Study (HRS) data into actionable system intelligence. Instead of static reporting, teams get decision-ready views of trajectory risk, transition pressure, and intervention timing.
What teams get
A unified picture of risk trajectories, intervention windows, and model confidence that supports strategic, clinical, and program-level decisions.
Delivery model
Iterative briefs, reproducible artifacts, and practical implementation guidance for teams moving from exploratory analysis to operating decisions.
Toolset and output snapshots
Representative captures from the Health DKM toolchain, including participant-level views, state-surface outputs, and geometric analysis panels.
Planned workstreams
Data architecture
- Cohort and variable harmonization across waves
- Missingness strategy and uncertainty tracking
- Feature lineage and reproducible transforms
Modeling and inference
- Latent state and transition structure estimation
- Risk curvature and trajectory segmentation
- Counterfactual intervention sensitivity testing
Validation
- Out-of-sample longitudinal checks
- Robustness under alternative assumptions
- Interpretability review against domain priors
Outputs
- Technical briefs and method notes
- Reproducible analysis artifacts
- Decision-facing summary views