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Renal dysfunction significantly impairs the renal clearance of drugs, leading to potential complications in drug therapy. Renal failure, which can be caused by various factors, poses a significant challenge in the elimination of drugs from the body.
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The kidneys are essential organs in the human body, performing a myriad of tasks that maintain homeostasis and overall health.
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使用人工智能预测慢性病进展.

Mario A Isaza-Ruget1, Nancy Yomayusa2, Camilo A González3

  • 1Pathology and clinical laboratory. INPAC research group. Clinica Colsanitas. Keralty group, Fundación Universitaria Sanitas, Bogotá, Colombia.

BMC nephrology
|April 26, 2024
PubMed
概括

机器学习模型预测慢性病 (CKD) 的进展和替代疗法 (RRT) 的需要. 一个时间到事件模型显示,在治疗慢性病患者的临床应用中具有前景.

关键词:
人工智能的人工智能是人工智能.机器学习 机器学习预测 预测 预测功能不良 功能不良脏替代疗法是指脏替代疗法.

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科学领域:

  • 腎臟病學 (nephrology) 是一種醫學專業.
  • 医疗信息学 医疗信息学
  • 数据科学数据科学数据科学

背景情况:

  • 慢性病 (CKD) 进展预测工具缺乏临床实用性.
  • 开发高级CKD的准确预测模型至关重要.

研究的目的:

  • 开发和验证用于预测CKD进展的机器学习模型.
  • 预测3-5期慢性瘤患者需要替代疗法 (RRT) 的需要.

主要方法:

  • 追溯性,观察性队列研究.
  • 利用了CKD患者的人口,临床和实验室数据.
  • 开发并比较了三种机器学习预测模型.

主要成果:

  • 模型1 (4.5年风险) 的F1得分为0.82 (RRT),0.53 (阶段进展) 和0.55 (eGFR减少).
  • 模型2 (时间到事件) 显示C指数为0.89 (RRT),0.67 (阶段进展) 和0.67 (eGFR减少).
  • 模型3 (减少的模型2) 显示C指数为0.68 (RRT),0.68 (阶段进展) 和0.88 (eGFR减少).

结论:

  • 时间到事件模型有效预测了5年内CKD进展结果.
  • 这种模型可以帮助预测CKD患者的不良事件的发生和时间.