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Drug Dosing: Geriatric Patients01:15

Drug Dosing: Geriatric Patients

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Elderly individuals encompass a diverse population with varying degrees of age-related physiological changes. Defining the elderly presents challenges, as the geriatric population is often arbitrarily categorized as individuals older than 65. However, many individuals in this group lead active and healthy lives, with an increasing number surpassing 85 years and falling into the older elderly category. Physiological changes associated with aging impact performance capacity and homeostatic...
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In geriatric patients, renal physiology undergoes significant changes, including diminished renal blood flow and a lower glomerular filtration rate (GFR), leading to alterations in medication clearance. Drugs such as aminoglycoside antibiotics, lithium, and digoxin, which rely on glomerular filtration for removal from the body, particularly impact pharmacokinetics. These drugs tend to have slower clearance rates in older adults, necessitating careful dosage considerations.Evaluation of renal...
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相关实验视频

Updated: May 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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在老年患者中使用机器学习方法预测基于营养不良的贫血.

Mehmet Göl1, Cemal Aktürk2, Tarık Talan2

  • 1Department of Physiology, Faculty of Medicine, Gaziantep Islam Science and Technology University, Gaziantep, Turkey.

Journal of evaluation in clinical practice
|September 23, 2024
PubMed
概括

机器学习使用营养不良和活动数据准确地预测老年人的贫血,即使没有血液测试. 这有助于老年患者的早期诊断和治疗.

关键词:
J48 J48 J48 J48 J48 J48 J48 J48 J48 J48 J48 J48随机的森林 随机的森林贫血 贫血 是一种疾病.人工智能的人工智能是人工智能.机器学习是机器学习.

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科学领域:

  • 老年医学 老年医学
  • 计算医学是一种计算医学.
  • 营养科学 营养科学

背景情况:

  • 老年人的贫血,通常是由于营养缺乏 (铁,叶酸,维生素B12),增加了发病和死亡的风险.
  • 早期的贫血诊断和治疗对于改善老年患者的治疗结果至关重要.
  • 这项研究的重点是预测门诊老年患者群体的贫血.

研究的目的:

  • 用机器学习 (ML) 方法预测老年患者的贫血诊断.
  • 为了评估ML模型的性能,有和没有血图数据.
  • 为未来的老年贫血研究提供有价值的数据集.

主要方法:

  • 使用ML对血液图,生物化学,营养不良和身体/认知活动得分进行贫血分类.
  • ML算法的比较,包括J48和随机森林.
  • 仅使用非血液测试属性 (营养不良,体力活动) 进行预测性表现分析.

主要成果:

  • 在使用所有可用的数据时,J48算法实现了97.77%的准确性.
  • 除了血液图数据外,随机森林算法只使用营养不良和体力活动得分,实现了85.39%的准确性.
  • 该数据集包括438名老年患者的观察.

结论:

  • 老年患者的贫血可以在不依赖血液图数据的情况下准确预测.
  • 这项研究强调了ML在老年人非侵入性贫血预测方面的潜力.
  • 共享的数据集有助于进一步研究ML方法和老年疾病预测.