Women’s Health DAO
Get Some Rest! We'll Take Care of Your Health!
1) The Technology Innovation
We propose a privacy-preserving analytics stack that converts heterogeneous wearable time-series (HR/HRV, sleep, activity, temperature) and brief self-reports into validated digital biomarkers tailored to women’s health (e.g., cycle dynamics, sleep regularity/quality, stress load). The core innovation is a signal-quality–aware (SQI) feature engine and interpretable ML that (i) harmonizes devices and sampling rates, (ii) resists motion/artifact noise, and (iii) yields clinically-meaningful, reproducible endpoints without handling PHI or running clinical trials in Phase I. Today’s wellness scores lack evidentiary value; trial endpoints are costly and slow. Our approach focuses on analytic validity first, with preregistered evaluation and artifacted releases (code, redacted samples) enabling third-party verification. Success generalizes to menopause/perinatal sleep and cardio‑metabolic risk, catalyzing a new class of auditable digital endpoints for population analytics.
2) The Technical Objectives and Challenges
Objective A — Curate & harmonize de‑identified longitudinal datasets (N≈2,000–5,000); compute SQI; align metadata.
Challenges: device heterogeneity; missingness; drift/bias.
Approach: canonical windows; artifact‑robust filters; time‑sync heuristics; bias diagnostics.
Objective B — Engineer & evaluate 2–3 women’s‑health endpoints (e.g., sleep regularity index; cycle‑linked recovery; stress‑load proxy).
Challenges: label scarcity; confounding across devices/demographics; overfitting.
Approach: interpretable features (HRV domains, actigraphy motifs, circadian stability), preregistered evaluation, cross‑device/domain generalization tests, uncertainty quantification.
Objective C — Reproducibility & Phase II plan: containerized reference pipeline; redacted sample dataset; evaluation harness; stakeholder interviews for Phase II clinical validity (outside Phase I scope).
Milestones: QC’ed dataset with SQI dashboard; endpoints meet pre‑set targets (e.g., ICC≥0.75; AUROC/MAE thresholds); reproducibility report.
3) The Market Opportunity
Customers: payers, self‑insured employers, population‑health/RPM vendors, and research networks seeking auditable endpoints. Problem: wellness scores are opaque and weakly validated; clinical‑trial endpoints are expensive/slow. Beachhead: B2B research licensing/API for analytics‑only use (de‑identified), supporting sleep/stress/cycle‑related burden and productivity. TAM: multi‑billion employer‑benefits/RPM analytics with rising demand for digital endpoints in RWE. Differentiation: device‑agnostic harmonization + SQI, interpretable endpoints with reproducible evidence, and privacy‑by‑design sharing (controlled‑access datasets; public code/specs). Early traction via design‑partner letters; pricing as metered API/license.
4) The Company and Team
US SME focused on privacy‑preserving time‑series analytics and digital endpoints. PI: technical founder (≥20 hrs/week) with expertise in time‑series ML and secure data engineering. Advisors (part‑time): women’s‑health and sleep experts; security/privacy advisor. Why us: prior deployments in TS/edge analytics; open‑science mindset (artifacted evaluations). Phase I funds: data engineering & harmonization, endpoint R&D, reproducibility tooling, non‑interventional UX probe, and Phase II planning for clinical validity (outside Phase I).
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