arXiv Machine Learning June 25, 2026 By Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed, Yi Guo, Thomas J. George, Jiang Bian, Yonghui Wu

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure

Read the original on arXiv Machine Learning →

arXiv:2403. 11425v4 Announce Type: replace Abstract: Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 2

LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

arXiv:2606. 00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment.

By Jeba Maliha, Md Rafiul Kabir

llms

arXiv AI

Jun 18

Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis

arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.

By Soheyl Bateni, Maryam Abdolali

llms safety

arXiv Machine Learning

Jun 9

From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model

arXiv:2606. 08945v1 Announce Type: new Abstract: We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model.

By Nicholas I-Hsien Kuo, Blanca Gallego, Louisa Jorm

llms diffusion fine-tuning efficiency

Hugging Face Trending Papers

Jun 8

From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model

We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model. We propose a text-based survival modelling pipeline in which structured clinical covariates are converted into text prompts and a Qwen-based large language model is fine-tuned to generate patient-specific survival risk using Cox model predictions as a training target.

llms diffusion fine-tuning efficiency

arXiv AI

Jul 20

Large Language Models as Unified Multimodal Learners for Clinical Prediction

arXiv:2607. 15380v1 Announce Type: cross Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities.

By Ajay Madhavan Ravichandran, Bilgin Osmandoja, Klemens Budde, Klaus Netter, Tobias Strapatsas, Aljoscha Burchardt, Sebastian M\"oller, Roland Roller

llms fine-tuning multimodal

arXiv Computation and Language

3d ago

Large Language Models are Approximate Survival Estimators

arXiv:2609.38181v1 Announce Type: new Abstract: Survival analysis estimates time-to-event outcomes from patient covariates and is widely used for medical risk assessment. Patients seeking prognostic...

By Juan M Zambrano Chaves, Peniel Argaw, Risa Ueno, Carlo Bifulco, Kristina Young, Rom Leidner, Tristan Naumann, Hoifung Poon

llms benchmarks

One email a morning, machine-written

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LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

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From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model

From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model

Large Language Models as Unified Multimodal Learners for Clinical Prediction

Large Language Models are Approximate Survival Estimators