Bayesian N-of-1 learning. Causal inference. Individual treatment effect estimation. Five ML models behind a single API surface.
Current clinical tools operate on cohort averages. Kairos operates where clinical decisions actually happen — at the individual level.
Regression-based tools identify associations — they cannot tell you whether a protocol caused a biomarker shift or merely correlated with it. Dragonnet-level causal inference requires infrastructure most teams cannot build in-house.
A protocol effective for the majority fails the outliers — often the patients with the highest unmet need. Individual treatment effect estimation identifies who responds before you commit to a 90-day protocol.
A single predicted value is not actionable — it is overconfident. Calibrated conformal prediction intervals tell clinicians the range within which the true response will fall, at any requested confidence level.
Five production models. One API surface. Designed to plug into your clinical workflow without rebuilding your data pipeline.
Five-class treatment response prediction with conformal prediction intervals. Every output includes a calibrated uncertainty range — never a bare point estimate.
Individual treatment effect estimation via twin XGBoost T-Learner. Identifies which protocol produces a measurable response for this specific patient.
Deep causal model jointly estimates propensity and outcome. Separates causation from correlation in observational clinical data at scale.
Composite biological age from 19 biomarkers with gender-specific calibration. Delta-age output quantifies the gap between chronological and biological age. ICC ≥ 0.75.
Bayesian posterior updates with every blood draw. Priors encode 45+ published meta-analyses. Each observation refines the patient-specific model — no retraining.
All five models accessible via one authenticated REST endpoint. Structured JSON. No proprietary SDK. GCP Cloud Run. 47ms p95 latency.
Send patient biomarker data as structured JSON to a single authenticated endpoint. No proprietary SDK. No custom data pipeline. HIPAA-aware architecture.
Receive individual treatment effects, biological age, risk class, and Bayesian posterior — each with calibrated confidence intervals. Latency under 50ms at p95.
Each new observation updates the patient's Bayesian posterior automatically. Prediction accuracy improves with every blood draw. No manual retraining required.
// Request
{
"patient_id": "pt_8a3f92",
"biomarkers": {
"hba1c": 5.4,
"hsCRP": 0.8,
"IL6": 2.1,
"dheas": 180,
"testosterone": 420
// ...19 total biomarkers
},
"treatment": "high_dose_nad"
}
// Response — 47ms p95
{
"ite": {
"estimate": 0.34,
"ci_95": [0.18, 0.51]
},
"bio_age": {
"delta": -4.2,
"chronological": 52
},
"risk_class": "responder",
"confidence": 0.87,
"model_version": "v2.4.1"
}Priors sourced from peer-reviewed meta-analyses. Models validated against MIDUS and NHANES cohorts. Every prediction traceable to its evidence base.
Every prior in the registry is encoded with a PMID or DOI. Standard deviations are inflated 20% for conservatism.
Marginal coverage is guaranteed at the requested level without distributional assumptions. Valid on finite samples.
Biomarker correlations and biological age slopes calibrated against MIDUS (n=7,000+) and NHANES. 20–30% real-world degradation accepted and documented.
Replace protocol intuition with individual-level evidence. Predict which NAD+, peptide, or hormonal protocol produces a measurable response for each patient before committing to a 90-day program.
Plug-and-play ML engine behind your product. Integrate treatment effect estimation, biological age, and risk prediction via REST — without hiring a team of biostatisticians or maintaining model infrastructure.
Design N-of-1 trials with Bayesian adaptive stopping rules. Every patient serves as their own control. Posterior updates reduce required sample sizes without sacrificing statistical validity.