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Updated: Feb 6, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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可解释的人工智能揭示了秋季预测中的时间风险路径:从多地平线机器学习模型中提取临床见解

Masoud Khani1, David R Friedland2, Michael Widlansky3

  • 1Department of Health Informatics & Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

GeroScience
|February 4, 2026
PubMed
概括

跌倒是老年人受伤的主要原因. 这项研究使用了跨多个时间框架的机器学习 (ML) 来表明跌倒风险是动态的,具有短期风险的急性因素和长期脆弱性的慢性条件.

关键词:
电子健康记录 (EHR) 是一种电子医疗记录.可解释的人工智能 (XAI)跌倒风险预测 跌倒风险预测老年人的综合征.机器学习在医疗保健中的应用时间风险建模模型

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