相关实验视频

Updated: Jan 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

在65岁以上的成年人中预测传统中医成分:一种机器学习方法

Chen Sun¹, Zhen Yu², Zong-Yuan Ge²

¹School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.

Journal of integrative medicine

|November 16, 2025

概括

机器学习准确地预测了老年人中偏见的传统中医 (TCM) 构成. 年龄和血液参数等关键预测因素使得个性化预防策略能够改善老年人医疗保健.

科学领域:

老年学是指老年学的学科.
综合医学是一个整体的医学.
计算健康计算健康

背景情况:

偏见的传统中医 (TCM) 宪法在老年人群中很普遍.
对这一群体而言,需要有效的健康管理策略.

研究的目的:

用机器学习预测65岁及以上个体中偏见的TCM构成.
确定这个年龄组中偏见的TCM宪法的关键预测因素.

主要方法:

这是一项针对中国上海4403名老年人的横截面研究.
收集的人口,生活方式和临床数据.
训练并比较了六种机器学习模型:随机森林 (RF),梯度增强机 (GBM),支持向量机 (SVM),极端梯度增强 (XGBoost),自适应增强分类器 (AdaBoost) 和后勤回归 (LR).

主要成果:

29.2%的参与者有偏见的TCM宪法.
随机森林 (RF) 实现了最高的预测性能 (AUC = 0.847).
关键预测因素包括年龄,心率,单细胞,氨酸转移酶,血小板分布宽度,总胆红素和肌素.

结论:

高患病率需要针对老年人进行有针对性的健康管理.
机器学习模型,特别是射频,可以准确预测偏见的TCM构成,帮助早期识别.
识别的预测因素为基于TCM的老年医疗保健提供了个性化预防策略的见解.

关键词:

年长的老年人.机器学习是机器学习.传统中国医学的宪法.

更多相关视频

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

相关概念视频

Drug Dosing: Geriatric Patients

Drug Dosing: Geriatric Patients

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...