Healthcare Early-Warning Analytics
Use synthetic healthcare telemetry to evaluate whether machine learning models can detect non-adherence patterns and post-discharge readmission risks.
The Problem
What difficult challenge is this experiment tackling?Preventable 30-day hospital readmissions impose billions in unnecessary healthcare costs and cause severe patient suffering when early warning signs go unnoticed.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?Analyzing non-clinical behavioral markers (prescription pickup delays, missed follow-ups, synthetic vital drift) can flag at-risk patients 7 days prior to emergency readmission.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human InterventionAUC-ROC score of 0.83 on synthetic test sets; SHAP values provide clear human-interpretable factors for every alert.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsStrict usage of synthetic, open-source de-identified research datasets (MIMIC/Synthea); zero live patient connectivity.
Lessons Learned & Observations
Empirical findings from prototyping and testingFeature engineering on gap days between prescription refills proved to be the highest predictive factor.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesIncorporate ambient indoor sensor telemetry (synthetic step count drop) as an early warning factor.