深度学习模型可预测阿尔茨海默病进展并进行不确定性估计 · 已追踪 4 个来源

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Arya Hariharan, Shreyank N Gowda, Anala M R · 2026-06-24 04:00

   利用深度学习进行阿尔茨海默病进展的不确定性感知纵向预测

   arXiv:2606.24604v1 Announce Type: new Abstract: Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep lear…

2. arXiv cs.AI TIER_1 English(EN) · Anala M R · 2026-06-23 14:04

   利用深度学习进行阿尔茨海默病进展的不确定性感知纵向预测

   Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-st…

3. Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-23 14:04

   利用深度学习进行阿尔茨海默病进展的不确定性感知纵向预测

   Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-st…

4. Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-14 12:33

   用于阿尔茨海默病进展的阶段感知因果建模的具有潜在时间嵌入的贝叶斯网络

   Alzheimer's disease (AD) progression is often described through the amyloid-tau-neurodegeneration, or AT(N), cascade. However, most longitudinal models represent this cascade either as a fixed sequence of biomarkers or as a black-box forecasting task. This makes it difficult to d…