arXiv subjects

Richik Chakraborty

Publications and source records attributed to Richik Chakraborty.

4 recordsLinked to original sources

arXiv2026

Project Hermes: A Model-Agnostic Validation Layer for Wearable Health Prediction Systems

The deployment of wearable-based health prediction systems has accelerated rapidly, yet these systems face a fundamental challenge: they generate alerts under substantial uncertainty without principled mechanisms for user-specific validation. While large language models (LLMs) have been increasingly applied to healthcare tasks, existing work focuses predominantly on diagnosis generation and risk prediction rather than post-prediction validation of detected signals. We introduce Project Hermes, a model-agnostic validation layer that treats signal confirmation as a sequential decision problem. Hermes operates downstream of arbitrary upstream predictors, using LLM-generated contextual queries to elicit targeted user feedback and performing Bayesian confidence updates to distinguish true positives from false alarms. In a 60-day longitudinal case study of migraine prediction, Hermes achieved a 34% reduction in false positive rate (from 61.7% to 12.5%) while maintaining 89% sensitivity, with mean lead time of 4.2 hours before symptom onset. Critically, Hermes does not perform diagnosis or make novel predictions; it validates whether signals detected by upstream models are clinically meaningful for specific individuals at specific times. This work establishes validation as a first-class computational problem distinct from prediction, with implications for trustworthy deployment of consumer health AI systems.

q-bio.QM↗

arXiv2026

Privacy-Preserving Cohort Analytics for Personalized Health Platforms: A Differentially Private Framework with Stochastic Risk Modeling

Personalized health analytics increasingly rely on population benchmarks to provide contextual insights such as ''How do I compare to others like me?'' However, cohort-based aggregation of health data introduces nontrivial privacy risks, particularly in interactive and longitudinal digital platforms. Existing privacy frameworks such as $k$-anonymity and differential privacy provide essential but largely static guarantees that do not fully capture the cumulative, distributional, and tail-dominated nature of re-identification risk in deployed systems. In this work, we present a privacy-preserving cohort analytics framework that combines deterministic cohort constraints, differential privacy mechanisms, and synthetic baseline generation to enable personalized population comparisons while maintaining strong privacy protections. We further introduce a stochastic risk modeling approach that treats re-identification risk as a random variable evolving over time, enabling distributional evaluation through Monte Carlo simulation. Adapting quantitative risk measures from financial mathematics, we define Privacy Loss at Risk (P-VaR) to characterize worst-case privacy outcomes under realistic cohort dynamics and adversary assumptions. We validate our framework through system-level analysis and simulation experiments, demonstrating how privacy-utility tradeoffs can be operationalized for digital health platforms. Our results suggest that stochastic risk modeling complements formal privacy guarantees by providing interpretable, decision-relevant metrics for platform designers, regulators, and clinical informatics stakeholders.

cs.CR↗

arXiv2026

Progressive Bayesian Confidence Architectures for Cold-Start Personal Health Analytics: Formalizing Early Insight Through Posterior Contraction and Risk-Aware Interpretation

Personal health analytics systems face a persistent cold-start dilemma: users expect meaningful insights early in data collection, while conventional statistical inference requires data volumes that often exceed engagement horizons. Existing approaches either delay inference until fixed statistical thresholds are met -- leading to user disengagement -- or surface heuristic insights without formal uncertainty quantification, risking false confidence. We propose a progressive Bayesian confidence architecture that formalizes early-stage inference through phased interpretation of posterior uncertainty. Drawing on Bayesian updating and epistemic strategies from financial risk modeling under sparse observations, we map posterior contraction to interpretable tiers of insight, ranging from exploratory directional evidence to robust associative inference. We demonstrate the framework's performance through controlled experimentation with synthetic N-of-1 health data, showing that calibrated early insights can be generated within 5--7 days while maintaining explicit epistemic humility. Compared to fixed-threshold baselines requiring 30+ days of data, the proposed approach yields earlier directional signals (mean: 5.3 vs 31.7 days, p<0.001) while controlling false discovery rates below 6% (5.9% at day 30) despite 26-day earlier detection, compared to 0% FDR for fixed-threshold baselines that delay insights by 30 days. In addition, we show strong uncertainty calibration (76% credible interval coverage for ground-truth correlations at day 90). This work contributes a methodological framework for uncertainty-aware early inference in personalized health analytics that bridges the gap between user engagement requirements and statistical rigor.

stat.ME↗

arXiv2026

Beyond P-Values: Importing Quantitative Finance's Risk and Regret Metrics for AI in Learning Health Systems

The increasing deployment of artificial intelligence (AI) in clinical settings challenges foundational assumptions underlying traditional frameworks of medical evidence. Classical statistical approaches, centered on randomized controlled trials, frequentist hypothesis testing, and static confidence intervals, were designed for fixed interventions evaluated under stable conditions. In contrast, AI-driven clinical systems learn continuously, adapt their behavior over time, and operate in non-stationary environments shaped by evolving populations, practices, and feedback effects. In such systems, clinical harm arises less from average error rates than from calibration drift, rare but severe failures, and the accumulation of suboptimal decisions over time. In this perspective, we argue that prevailing notions of statistical significance are insufficient for characterizing evidence and safety in learning health systems. Drawing on risk-theoretic concepts from quantitative finance and online decision theory, we propose reframing medical evidence for adaptive AI systems in terms of time-indexed calibration stability, bounded downside risk, and controlled cumulative regret. We emphasize that this approach does not replace randomized trials or causal inference, but complements them by addressing dimensions of risk and uncertainty that emerge only after deployment. This framework provides a principled mathematical language for evaluating AI-driven clinical systems under continual learning and offers implications for clinical practice, research design, and regulatory oversight.

stat.ME↗