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LLM-Augmented Computational Phenotyping of Long Covid

arXiv 2603.18115 ↗First posted 18 Mar 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Phenotypic characterization is essential for understanding heterogeneity in chronic diseases and for guiding personalized interventions. Long COVID, a complex and persistent condition, yet its clinical subphenotypes remain poorly understood. In this work, we propose an LLM-augmented computational phenotyping framework ``Grace Cycle'' that iteratively integrates hypothesis generation, evidence extraction, and feature refinement to discover clinically meaningful subgroups from longitudinal patient data. The framework identifies three distinct clinical phenotypes, Protected, Responder, and Refractory, based on 13,511 Long Covid participants. These phenotypes exhibit pronounced separation in peak symptom severity, baseline disease burden, and longitudinal dose-response patterns, with strong statistical support across multiple independent dimensions. This study illustrates how large language models can be integrated into a principled, statistically grounded pipeline for phenotypic screening from complex longitudinal data. Note that the proposed framework is disease-agnostic and offers a general approach for discovering clinically interpretable subphenotypes.

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References · 15

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3 more
  1. 2020Efficient PAC Learning from the Crowd with Pairwise Comparisonscited by 7
  2. 2023StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variablescited by 4
  3. 2024Gradient Aligned Regression via Pairwise Lossescited by 1

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