Cognitive Machine Learning for Patient-First Modeling in Clinical Research

Shashank Uttrani, Shruti Kaushik, Martin White

Keywords: cognitive machine learning, patient-first modeling, cognitive-integrated clinical trials, behavioral foundational models, clinical research

Abstract: Clinical trials remain the cornerstone of evidence-based medicine. Yet their prevailing methods often reduce patients to statistical data points, overlooking cognition-driven factors such as consent comprehension, assessment fatigue, and telescoping in adverse-event (AE) reporting. We propose a new approach that integrates cognitive science and large language models (LLMs) to model patient comprehension, recall, preferences, and incentives. Building on behavioral foundation models as starting priors, we introduce a cognitive ML model for clinical research: a thin, patient-first layer that adapts foundation models to trial workflows via clinical cover stories such as consent with brief teach-backs, AE narratives with temporal anchors, preference trade-offs under framing, and bias-aware disclosure prompts. The layer overlays existing PDFs, ePRO apps, and AE logs, adding guardrails such as calibration thresholds, clinician deferral, and auditability, rather than replacing infrastructure. We outline a roadmap from lightweight cognitive overlays to human-in-the-loop integration, and ultimately, cognitive-integrated trials with governance and regulatory alignment. An abridged AE-reporting case study shows increased AE yield and improved timing fidelity while enforcing calibration and subgroup-parity gates. The next generation of clinical trials must be not only statistically rigorous but cognitively grounded and inclusive.

Submission Number: 95