相关实验视频

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
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

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预测艾滋病毒感染者首次和多次抗逆转录病毒治疗中断:使用Cox和可解释的机器学习模型进行比较生存分析

Donald Salami1, Emily Koech2, Janet M Turan3

  • 1Center for International Health, Education and Biosecurity, Institute of Human Virology, School of Medicine, University of Maryland, 725 W Lombard St, Baltimore, MD, 21201, United States, 1 410-706-8614.

JMIR medical informatics
|February 4, 2026
PubMed
概括

机器学习模型在预测抗逆转录病毒疗法 (ART) 患者的治疗中断方面明显优于传统的考克斯比例危险模型. 这些先进的模型提供了个性化的洞察力,以改善患者在护理中的保留.

关键词:
抗逆转录病毒药物治疗可以解释的机器学习机器学习是机器学习.递归分区是指递归分区.治疗中断,治疗中断.

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