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MultiSigBERT: Beyond Survival Analysis through Multimodal and Sequential Modeling in Oncology — arXiv:2608.16972

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16972
License (as archived): CC0-1.0
Archived: 2026-10-18T14:48:26.654Z
SHA-256 of the archived text: c373721de88f23457d9434a9596d939279178197767be87ed30d0c4b939135f1
Stable URL of this page: https://metraipolis.com/documento/dc_7f800bbafb08

How to cite

MultiSigBERT: Beyond Survival Analysis through Multimodal and Sequential Modeling in Oncology — arXiv:2608.16972 — arXiv (en), https://arxiv.org/abs/2608.16972 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7f800bbafb08 (Metraipolis archive dc_7f800bbafb08, sha256 c373721de88f23457d9434a9596d939279178197767be87ed30d0c4b939135f1)

BibTeX

@misc{dc7f800bbafb08,
  title = {MultiSigBERT: Beyond Survival Analysis through Multimodal and Sequential Modeling in Oncology — arXiv:2608.16972},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16972},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7f800bbafb08; sha256 c373721de88f23457d9434a9596d939279178197767be87ed30d0c4b939135f1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16972. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Machine learning has become an essential component of modern healthcare, where the integration of heterogeneous data sources offers unprecedented opportunities to improve clinical decision-making. Electronic Health Records (EHR) contain complementary information -- including narrative clinical reports, numerical measurements, and structured variables -- yet most survival models remain limited to a single modality or fail to exploit the temporal nature of patient trajectories. We propose MultiSigBERT, a unified framework for multimodal sequential survival modeling in oncology based on path signature representations. Here, narrative medical reports (free-text) are converted into sentence embeddings by extracting and averaging contextual word embeddings. These representations are then compressed via modality-specific PCA and concatenated with structured covariates to form joint temporal trajectories which are then encoded using the Signature transform, a tool from Rough Paths theory that efficiently captures higher-order temporal interactions across modalities without supervision needed. The computed Signature features are finally incorporated as high dimensional features into a LASSO-regularized Cox model to estimate individualized risk scores. The performance of our novel MultiSigBERT pipeline is illustrated on the analysis of a real-world oncology cohort from the Léon Bérard Center, comprising over 120,000 medical reports and structured records from more than 2,500 patients. The model achieves a concordance index of 0.743 (sd 0.029) on an independent test set, demonstrating the benefit of jointly modeling multimodal temporal dynamics together with patient-level geometric structure for survival prediction.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-19 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.16972 — 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 2026-10-18T14:47:59.373Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-19?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).