Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling

2026-08-17

AI Summary

This study addresses the challenge of acquiring and formalizing expert knowledge for longitudinal modeling of rare diseases by proposing a novel framework that employs Large Language Models (LLMs) as synthetic clinical experts to supervise Variational Autoencoder (VAE) training. Through a differentiable surrogate model, clinical priors are integrated into representation learning, enabling semantic-preserving latent space optimization. This approach significantly enhances both clinical fidelity and predictive performance, reducing SMA type label inconsistency from 11% to 7% and achieving superior predictions of motor function milestones compared to baselines. These findings validate the efficacy of LLM-driven knowledge augmentation in data-scarce scenarios, offering a promising new pathway for precision modeling in rare disease research.

Abstract

Due to the limited amount of information, modeling longitudinal rare-disease data can benefit from integrating clinical knowledge. Yet, elicitation of expert knowledge and formalization for model fitting is challenging, in particular due to limited time of clinical experts. To nevertheless make domain knowledge accessible during model fitting, we use large language models (LLMs) as synthetic clinical experts to supervise a variational-autoencoder-based approach that learns low-dimensional latent summaries of visit-level observations. Specifically, LLMs are queried offline on textual descriptions of patient observations to obtain judgments, e.g., the suspected clinical category. To improve the variational autoencoder fit, we train a differentiable surrogate model on these judgments and augment the loss function to encourage reconstructions that preserve the clinical-label distribution of their corresponding input profile. In an application to longitudinal motor-function assessments from children with spinal muscular atrophy, we map visit-level clinical profiles to low-dimensional representations that are linked by a multivariate mixed-effects model. The synthetic expert loss discourages reconstructions that remain numerically close in data space but alter the clinical interpretation of the reconstructed motor function profile, such as by crossing a disease-type boundary. We thus reduced disagreement between original and reconstructed SMA type labels from about 11 to 7 percent. Furthermore, informing the latent representation by the synthetic expert improved prediction of motor function milestones compared with unsupervised latent representations and a data-level baseline. These results suggest that incorporating LLMs into model fitting can make clinical knowledge available to representation learning and improve clinical faithfulness for longitudinal rare-disease data.

Problem

• Longitudinal rare-disease modeling
• Clinical knowledge elicitation
• Representation learning
• Clinical faithfulness

Innovation

• Synthetic Clinical Experts
• Differentiable Surrogate Model
• Variational Autoencoder
• Clinical Faithfulness
• Longitudinal Rare-Disease Modeling