Paper

Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data

Unreadunread

AI summary

arXiv:2606. 23871v1 Announce Type: new Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a single institution, while privacy regulations restrict the centralization of patient data.

Reading status

Priority

About this paper

Authors: Natalia Moreno-Blasco, Anusha Ihalapathirana, Pekka Siirtola, Miguel Fernandez-de-Retana
Released: 2026-06-24 (15d ago)
Topic: Related to current project
Relation to my work: Read next because Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, under, eval, source, line, rate, compare, without. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.23871

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.