Medical research prototype · Science

Longitudinal symptom forecasting research.

For scientists exploring personal symptom rhythms, predictive uncertainty and performance against documented baselines. HerPattern is being developed toward clinically validated health technology; it remains a medical research prototype, not yet clinically validated or intended for diagnosis or treatment.

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Research to clinical evidence

Toward clinically validated health technology.

Scientific exploration is the starting point. Clinical use requires a separate body of evidence, safety evaluation and appropriate regulatory review.

The research models documented here are only the beginning, not the ceiling: they are a deliberately simple first generation, and HerPattern is developing proprietary forecasting models trained on longer personal histories, with forecast quality expected to improve as those models mature. Current results describe the research models evaluated in the open benchmark.

View current benchmark evidence

Current status: Medical research prototype
Clinical validation: Not yet established

What clinical translation would require

Independent, representative validation

Prospective evaluation in independent cohorts, with predefined outcomes and performance assessed across relevant patient groups. Small-cohort and synthetic results do not establish generalizability.

Clinical usefulness and safety

Evidence that a defined clinical use is meaningful and safe, including assessment of uncertainty, failure modes and how results are interpreted by patients and clinicians.

Governance and regulatory review

A defined intended use, appropriate ethics and consent processes, data protection, quality controls and any applicable regulatory assessment before clinical deployment.

Research participation

Interactive model

See persistence, switching and uncertainty move together.

An illustrative simulation of persistence, state changes and uncertainty, separate from the research models used for real forecasts.

Persistence φ 0.72How strongly today carries into tomorrow.State-switch rate 0.18Chance of moving between a calmer and a heavier regime.Observation noise σ 0.55Day-to-day measurement variability.

Regime switches in 60 days: 14
Illustrative 80% band width: ±1.76 points
Simulated mean burden: 4.87 / 10

Illustrative simulation only. It is a toy AR(1) process with two latent states, not the hierarchical Bayesian autoregression or switching state-space model used for real forecasts. those run in the external research service and are never reproduced in the browser.

Hierarchical Bayesian autoregression

Each participant's day-to-day symptom burden is modeled as an autoregressive process, but the per-participant parameters are partially pooled across the population using a hierarchical Bayesian model. This lets HerPattern personalize quickly: new participants borrow strength from population-level patterns, and the model shifts toward an individual's own history as more days are logged.

Switching state-space model

Symptom burden does not move smoothly all the time. People experience distinct latent states (e.g. calmer stretches vs. higher-burden stretches) with transitions between them. A switching state-space layer recovers these latent states and their transition probabilities, which are combined with the autoregressive component into the final probabilistic forecast.

Evaluation protocol

Models are evaluated using a leave-one-participant-out population fit combined with held-out early-history calibration and rolling-origin evaluation, so forecasts are always tested against data the model was not trained on, at multiple points across a participant's timeline, to avoid overstating performance.

Metrics are compared against documented baselines, including simple persistence, personal mean, and cycle-day heuristics, at horizons of up to 3 days (8 participants evaluated in the current release). See the published metrics below for the current published metrics.

Uncertainty

Every forecast is reported with an 80% prediction interval and a plain-language confidence label, not just a point estimate. Uncertainty widens automatically when history is short, when recent logging has gaps, or when a participant's pattern is inherently more variable.

Limitations

Observational associations are not causal effects.
Cohorts evaluated to date are small and do not establish clinical generalizability.
Synthetic smoke-test results are execution evidence only, not clinical performance. See the benchmark results below for current evaluation status.
Forecasts can be, and sometimes will be, wrong. They are one input among many, not a substitute for clinical judgment.
Observational associations are not causal effects.
The cohort is small and does not establish clinical generalizability.
Synthetic smoke-test results are execution evidence only, not clinical performance.

Medical boundaries

HerPattern is not intended for diagnosis or treatment. It does not diagnose endometriosis or any other condition, detect medical flares, predict ovulation, or recommend medications or treatments. Forecasts describe the likelihood of low, moderate, or high self-reported symptom burden only. Read the full medical disclaimer.

Open benchmark

A reproducible, public evaluation.

Benchmark v0.1.0 · live read

Synthetic execution only

Synthetic execution results demonstrate that the benchmark pipeline runs. They do not constitute clinical evidence.

No clinical validation is claimed. Clinical performance requires authorized-access, real-data evaluation, which is still pending.

Evaluation methodology

The task is next-day forecasting of a participant’s overall symptom-burden score, together with a discrete symptom-pattern state and a prediction interval. Each forecast is produced from that participant’s history only up to the day before the target day, so no future information enters the prediction.

Evaluation is prospective and per participant: the model is fitted on the calibration window, then rolled forward one day at a time across the remaining history. A participant enters the evaluation only after meeting the minimum calibration requirement of 42 days; days with missing check-ins are skipped rather than imputed. A run is valid only when the same feature-contract and parameter-schema versions listed in the release summary were used end to end.

Every model is scored against the same reference baseline, persistence, on identical rows and horizons (1, 2, 3 day). Reported statistics are aggregates over all evaluated participants; individual-level records are never published. Statistical fitting runs entirely in the external modelling service, this page and the application only read published results.

Release summary

Benchmark version

0.1.0

Active model, public name

HerPattern Bayesian switching

Active model, machine name

herpattern_bayesian_switching

Model version

0.1.0

Feature-contract version

1.0

Parameter-schema version

1.0

Personalization mode

Partial pooling (hierarchical Bayesian) with switching state-space

Evaluation protocol

leave-one-participant-out population fit plus held-out early-history calibration and rolling-origin evaluation

Supported horizons

1, 2, 3 day(s)

Supported symptom roster

Per-participant chip roster (personalized), see model card

Coverage requirements

≥ 42 calibration days

Interval method

80% bootstrap prediction interval

Calibration status

synthetic-smoke-test-only

Latest evaluation date

2026-07-19T11:47:16.882956+00:00

Dataset category

synthetic-smoke-test

Synthetic or real data

Synthetic

Reproducibility status

Not yet release-ready (6 open blocker(s))

Git commit

Not available in the current release

Published aggregate metrics

All published rows are shown, including horizons and baselines where HerPattern does not lead. Nothing is filtered.

Model	Horizon	MAE	MAE 95% CI	RMSE	Macro F1	80% coverage	vs persistence
HerPattern Bayesian switching	1d	0.047	0.037–0.058	0.065	0.856	90.0%	7.2%
Persistence	1d	0.051	0.043–0.061	0.069	0.856	86.7%	0.0%
Personal mean	1d	0.163	0.141–0.189	0.193	0.318	36.7%	-218.1%
Cycle-day baseline	1d	0.172	0.148–0.194	0.213	0.322	37.5%	-235.6%
HerPattern Bayesian switching	2d	0.085	0.075–0.094	0.115	0.758	79.2%	3.9%
Persistence	2d	0.089	0.079–0.098	0.120	0.751	79.2%	0.0%
Personal mean	2d	0.157	0.136–0.175	0.187	0.339	49.2%	-77.2%
Cycle-day baseline	2d	0.165	0.145–0.185	0.201	0.346	54.2%	-86.9%
HerPattern Bayesian switching	3d	0.124	0.110–0.141	0.164	0.652	71.7%	1.8%
Persistence	3d	0.127	0.113–0.146	0.168	0.658	77.5%	0.0%
Personal mean	3d	0.167	0.148–0.189	0.194	0.309	59.2%	-31.9%
Cycle-day baseline	3d	0.176	0.160–0.193	0.208	0.342	58.3%	-39.0%

Metric definitions

Each metric below is computed over the published prediction rows for the stated horizon. Definitions are given so the table can be read alongside the published results.

MAE(burden points, 0–10 scale): Mean absolute error between the predicted next-day symptom-burden score and the observed score. Lower is better.
MAE 95% CI(burden points): Bootstrap 95% confidence interval around the reported MAE. Intervals that do not overlap indicate a difference the evidence supports.
RMSE(burden points): Root mean squared error, which penalises large misses more heavily than MAE. Lower is better.
Macro F1(0–1): Macro-averaged F1 over the discrete symptom-pattern states, weighting each state equally regardless of how often it occurs.
80% coverage(percentage of days): Share of days where the observed value fell inside the published 80% prediction interval. Well-calibrated uncertainty sits near 80%.
vs persistence(percent change in MAE): Relative MAE change against the persistence baseline. Positive values mean lower error than the baseline.

Every model row is compared against the same reference baseline, persistence, tomorrow predicted as a repeat of today. The models measured here are a first generation research method; they are the beginning of a longer development path, not its end state.

Cite this benchmark

Version and date below come from the release currently loaded on this page.

Plain citation

HerPattern. HerPattern open benchmark (version 0.1.0), 2026-07-19. https://herpattern.space/benchmark

BibTeX

@misc{herpattern_open_benchmark,
  title        = {HerPattern open benchmark},
  author       = {HerPattern},
  year         = {2026},
  version      = {0.1.0},
  howpublished = {\url{https://herpattern.space/benchmark}},
}

Limitations

Observational associations are not causal effects.
The cohort is small and does not establish clinical generalizability.
Synthetic smoke-test results are execution evidence only, not clinical performance.

Open release blockers

mcphases_aggregate_results_present
ki_endolist_aggregate_results_present
nhanes_coverage_audit_present
live_deployment_evidence_present
demo_video_evidence_present
edge_parameters_are_not_real-data-validated