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Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling — arXiv:2608.16507

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16507
License (as archived): CC0-1.0
Archived: 2026-10-18T17:45:02.889Z
SHA-256 of the archived text: df9ad8e9b0b6529c94e69e9157c0913dfaa1ceeb2a42e51e77c521f89afc5b47
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How to cite

Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling — arXiv:2608.16507 — arXiv (en), https://arxiv.org/abs/2608.16507 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8034bb2f08fe (Metraipolis archive dc_8034bb2f08fe, sha256 df9ad8e9b0b6529c94e69e9157c0913dfaa1ceeb2a42e51e77c521f89afc5b47)

BibTeX

@misc{dc8034bb2f08fe,
  title = {Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling — arXiv:2608.16507},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16507},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8034bb2f08fe; sha256 df9ad8e9b0b6529c94e69e9157c0913dfaa1ceeb2a42e51e77c521f89afc5b47},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16507. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.16507 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T16:29:13.876Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).