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.
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
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
arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.
By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska
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
ViSTA is a lightweight adapter that adds irregular numerical measurements to a pretrained vision‑language model’s chart representations, enabling accurate clinical time‑series prediction without altering the base model’s parameters. On the MIMIC‑IV dataset, ViSTA outperforms other adaptations across four metrics for acute kidney injury and mortality prediction, achieving an AUC of 0.7376 with only 0.516 million trainable parameters. It also delivers strong temporal question‑answering performance, reaching 69.27% accuracy with significantly fewer trainable parameters than low‑rank adaptation methods.
By Junyi Gao, Yu Shi, Pingzhao Hu, Ewen M Harrison
arXiv:2608. 02803v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attention maps provide only local explanations: they indicate where a model focuses but not which histological features drive its predictions or how the model behaves across a patient cohort.
By Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu, William Lotter
arXiv:2608. 16594v1 Announce Type: new Abstract: Cancer survival prediction supports treatment planning, risk stratification, and follow-up management.
By Tianqi Xiang, Qixiang Zhang, Xinpeng Ding, Yi Li, Xiaomeng Li
MultiSigBERT is a unified framework that performs multimodal sequential survival modeling in oncology by integrating narrative clinical reports, numerical measurements, and structured variables. The method converts free-text reports into sentence embeddings, compresses them with modality-specific PCA, and concatenates them with structured covariates to create joint temporal trajectories. These trajectories are encoded using the Signature transform from Rough Paths theory, and the resulting high-dimensional features are fed into a LASSO-regularized Cox model, achieving a concordance index of 0.743 on an independent test set of over 2,500 patients.
By Paul Minchella, St\'ephane Chr\'etien, Guillaume Metzler, Lo\"ic Verlingue, R\'emi Vaucher
arXiv:2606. 09030v1 Announce Type: cross Abstract: Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both calibrated risk scores for patient triage and interpretable rationales that clinicians can verify.
By Hyeongwon Jang, Gyouk Chu, Changhun Kim, Joonhyung Park, Hangyul Yoon, Eunho Yang
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
arXiv:2604.26766v2 Announce Type: replace-cross
Abstract: Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly varia...
By Manar Aljohani, Brandon Ho, Kenneth McKinley, Dennis Ren, Xuan Wang