The GRACE Cycle: A General Large-Language-Model Framework for Phenotype Discovery with Unknown Cluster Number

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Abstract Phenotype discovery—the data-driven identification of clinically or biologically meaningful subgroups—is fundamental to precision medicine, but conventional clustering methods require the number of clusters K to be specified a priori and struggle with heterogeneous, multimodal, or longitudinal data. We introduce the GRACE Cycle (Generate hypothesis, Retrieve evidence, Align, Converge, Evaluate), a general large-language-model (LLM)-assisted framework for phenotype discovery in which a hypothesis, an LLM, and an evidence base are iteratively refined until they agree. The framework discovers K as an output through Graph-of-Thought (GoT) refinement, in which an LLM reads per-cluster summary cards plus a between-cluster similarity matrix and proposes one of three moves—SPLIT, MERGE, or COMMIT—over a spectral-clustering seed. Two technical contributions enable scale: (i) a four-component prompt template integrating pairwise comparison, fairness pre-processing, and structured JSON output, and (ii) a data-feeding strategy that compresses cohorts of 10 4 –10 6 entities into context-budget-respecting batches via k-nearest-neighbour graph sampling. We validate GRACE across three heterogeneous phenotyping problems: (1) longitudinal Long COVID subphenotyping in the NIH RECOVER cohort (n = 13,511), where GRACE recovers three clinically distinct subphenotypes (Protected, Responder, Refractory) with bootstrap Jaccard stability > 0.97 that are explained by a single autonomic/post-viral-fatigue axis (a 25-fold dysautono-mia gradient, dysautonomia adjusted OR = 13.4) and an accompanying collapse of wearable-measured physical activity; (2) motor subphenotyping of Parkinson’s disease from foot-sensor gait wearables (PhysioNet gaitpdb, n = 93), where GRACE discovers two gait subtypes without specifying K that are externally validated against withheld Timed-Up-and-Go (p = 0.002), Hoehn–Yahr stage (p = 0.03), and age; and (3) additional open wearable chronic-disease cohorts processed with the identical pipeline. Across domains, GRACE converges without prior knowledge of K, demonstrating that LLM-guided iterative reasoning offers a domain-agnostic alternative to conventional clustering when the number of phenotypes is unknown.

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