相关实验视频
Updated: Jan 28, 2026
11:21
Robotic Ablation of Atrial Fibrillation
Published on: May 29, 2015
20.2K
通过新型深度学习框架对CIED患者的亚临床心房预测
Yongying Lan1, Chengze Lin1, Ning Zhang1
1Department of Cardiovascular Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Journal of cardiovascular development and disease
|January 27, 2026
概括
一个新的AI模型,ResKAN-Attention,使用常规临床数据准确预测心脏植入式电子设备的患者的亚临床心房动 (SCAF). 从这个模型中得出的简化风险评分有助于早期的SCAF风险分层.
科学领域:
- 人工智能在心血管医学中的应用
- 机器学习用于预测诊断.
- 生物医学数据科学 生物医学数据科学
背景情况:
- 亚临床心房动 (SCAF) 是密码性中风的重要危险因素.
- 目前在心脏植入式电子设备 (CIED) 患者中SCAF的预测工具有限.
- 常规的临床数据有可能预测SCAF.
研究的目的:
- 开发一个新的深度学习框架,ResKAN-Attention,用于SCAF预测.
- 仅使用常规临床数据来预测CIED患者的SCAF.
- 创建一个可解释和临床适用的风险评分系统.
主要方法:
- 使用来自124名CIED患者的27个常规参数开发ResKAN-Attention模型.
- 一个双路径架构,将科尔摩戈罗夫-阿诺德网络 (KAN) 与一个多层感知子结合起来,通过交叉注意力融合.
- 使用五倍交叉验证和与基线模型进行比较的绩效评估;可解释性分析和知识蒸用于风险得分推导.
主要成果:
- 在12个月的随访期间,SCAF发病率为31.5% (39/124).
- ResKAN-Attention显著超过了基线模型 (交叉验证AUC为0.837,外部验证为0.788).
- 关键预测指标包括左心脏直径,性别,乳酸脱酶,BMI和高血压;简化风险得分实现了高预测能力 (AUC 0.882).
结论:
- ResKAN-Attention模型显示,SCAF预测具有增强的可解释性.
- 衍生风险评分为临床实践中早期风险分层提供了一个潜在的工具.
- 先进的AI可以有效地预测复杂的心血管事件,使用随时可用的临床数据.
相关概念视频
03:03Amyloid Fibrils
11.9K
Amyloid fibrils are aggregates of misfolded proteins. Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils.
Amyloid deposits were observed as early as 1639 in the liver and the spleen. In 1854, Rudolph Virchow performed iodine staining,...
Amyloid deposits were observed as early as 1639 in the liver and the spleen. In 1854, Rudolph Virchow performed iodine staining,...
11.9K
03:03Amyloid Fibrils
6.4K
6.4K
01:11Fibril-associated Collagen
3.4K
Fibril-associated collagens are a type of collagens present in the extracellular matrix with interrupted triple helices or FACIT (Fibril-associated collagens interrupted triple-helices). FACIT help connect and attach the collagen fibrils with each other as well as with other proteins of the extracellular matrix.
For example, the type II collagen fibrils in cartilage have covalently bound type IX fibril-associated collagens at regular intervals. Other types of fibril-associated collagens are...
For example, the type II collagen fibrils in cartilage have covalently bound type IX fibril-associated collagens at regular intervals. Other types of fibril-associated collagens are...
3.4K
02:27Predicting Molecular Geometry
45.7K
VSEPR Theory for Determination of Electron Pair Geometries
45.7K
01:03Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
01:14Avoidance Learning and Learned Helplessness
2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K