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相关概念视频

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration

178
Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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Factors Affecting Renal Clearance: Renal Impairment01:17

Factors Affecting Renal Clearance: Renal Impairment

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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.
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...
407

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相关实验视频

Updated: Jan 12, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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开发,外部验证和部署RFAN-ML:一种机器学习模型,用于估计脏切除术后的功能.

Jesse Persily1, Steven L Chang2, Chen Chen1

  • 1Department of Urology, NYU Grossman School of Medicine, New York, NY.

JCO clinical cancer informatics
|November 7, 2025
PubMed
概括

我们开发了一种机器学习 (ML) 模型,RFAN-ML,用于预测切除术后的功能. 这个工具有助于个性化患者护理和脏瘤患者的手术规划.

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科学领域:

  • 腎臟病學 (nephrology) 是一種醫學專業.
  • 在瘤学瘤学.
  • 数据科学数据科学数据科学

背景情况:

  • 部分切除术是小瘤的首选,但具有外科手术期间的风险.
  • 精确估计脏切除术后的功能对于患者咨询和外科决定至关重要.
  • 现有的预测模型往往缺乏外部验证和用户友好的界面.

研究的目的:

  • 开发和外部验证一个机器学习 (ML) 模型,RFAN-ML,用于估计腎切除术后的长期功能.
  • 为预测脏切除术后功能提供一个用户友好的工具.

主要方法:

  • 利用来自两个学术医学机构的数据.
  • 雇佣Boruta功能选择以确定关键预测因素:年龄,BMI,手术前功能和切除术类型.
  • 训练和评估了六个ML回归模型,选择了表现最好的模型为RFAN-ML.

主要成果:

  • 与现有基准相比,RFAN-ML表现优越或具有竞争力的表现.
  • 实现了根平均二次误差 (RMSE) 的16.6 (95% CI,15.6至17.5).
  • 使用R平方和平均绝对误差指标来评估性能.

结论:

  • 一个经过验证的ML模型RFAN-ML准确地预测了脏切除术后的功能.
  • 该模型可以在线获得,促进个性化的患者咨询和手术规划.
  • RFAN-ML有可能改善瘤患者的护理和治疗结果.