[在医学预测模型中解决阶级不平衡的当前方法和挑战]
X L Meng1, Y T Wang1, X Zhang1
1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China.
Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
|September 18, 2025
概括
医疗数据通常具有阶级不平衡,扭曲预测模型. 本综述涵盖了传统和新方法,如生成对抗网络,以改善大数据中的少数群体类检测,以获得更好的临床应用.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 大数据分析大数据分析
背景情况:
- 个性化医疗和大数据推动了对准确医疗预测模型的需求.
- 医学数据集中的阶级不平衡阻碍了模型的性能,特别是在少数阶级.
- 这会影响疾病的诊断,预后和风险分层的准确性.
研究的目的:
- 系统地审查解决医疗大数据中的阶级不平衡的方法.
- 引入先进的技术,如生成对抗网络和转移学习.
- 为研究人员提供有关选择适当策略的指导.
主要方法:
- 关于传统类失衡技术 (数据预处理,算法级) 的综合文献综述.
- 探索新的方法,包括生成对抗网络 (GAN) 和转移学习.
- 分析关键考虑因素和未来的研究方向.
主要成果:
- 传统方法提供了处理不平衡数据的基本策略.
- 像GAN和转移学习这样的新兴技术显示出增强少数群体阶级检测的希望.
- 为实践应用,介绍了策略的结构化概述.
结论:
- 解决阶级不平衡对于医学预测模型的临床实用性至关重要.
- 传统和新方法的组合可能是必要的,以获得最佳的性能.
- 需要进一步的研究来完善和验证医疗大数据的先进技术.
相关概念视频
Strategies for Assessing and Addressing Confounding
364
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
364
Classification of Illness
8.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.6K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
242
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
242
Kaplan-Meier Approach
577
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
577
Model Approaches for Pharmacokinetic Data: Physiological Models
249
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
249
Bias in Epidemiological Studies
1.3K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
1.3K

