English(EN) TEMPO: Transformers for Temporal Disease Progression from Cross-Sectional Data

TEMPO Transformer 模型可根据横断面数据预测疾病进展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

研究人员开发了 TEMPO，一种新颖的 Transformer 架构，旨在从横断面数据中对疾病进展进行建模。与依赖于严格假设且仅生成有序序列的先前方法不同，TEMPO 同时学习有序和连续事件序列。这种方法显著提高了推断疾病分期和事件排序的准确性，在合成基准测试中优于最先进的模型。 AI

影响引入了一种新的基于 Transformer 的疾病进展建模方法，有望提高医学研究中的诊断和预后准确性。

排序理由这是一篇描述特定科学应用新模型架构的研究论文。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Hongtao Hao, Joseph L. Austerweil · 2026-04-28 04:00

TEMPO：基于横断面数据的时序疾病进展Transformer模型

arXiv:2604.23368v1 Announce Type: new Abstract: Event-Based Models (EBMs) infer biomarker progression from cross-sectional data but typically only as ordinal sequences and rely on rigid model assumptions. We propose \textsc{Tempo}, a Transformer architecture that learns both ordi…