临床预测模型中类失衡的重新采样方法:一个范围审查协议
Osama Abdelhay1, Adam Shatnawi2, Hassan Najadat2
1Department of Data Science and Artificial Intelligence, Princess Sumaya University for Technology, Amman, Jordan.
PloS one
|November 3, 2025
概括
本综述综合了15年来对不平衡临床数据集的重新采样策略的研究. 它旨在确定哪些方法可靠地提高医疗人工智能模型的性能,解决医疗人工智能的关键差距.
科学领域:
- 医学的人工智能 (AI)
- 医疗保健中的机器学习
- 临床预测建模临床预测建模
背景情况:
- 临床数据集中的阶级不平衡 (<30%的阳性病例) 阻碍了医学预测模型的敏感性和公平性.
- 现有的数据级 (例如,SMOTE) 和算法级 (例如,成本敏感学习) 策略缺乏全面的实证证据.
- 这种跨疾病和模型的分散证据在可靠的医疗AI中造成了方法上的差距.
研究的目的:
- 对不平衡的临床数据进行重新采样策略的元回归进行范围系统审查.
- 绘制和量化总结15年 (2009-2024) 关于这些战略的研究.
- 解决有关可靠改善医疗AI性能的方法差距.
主要方法:
- 在主要数据库 (MEDLINE,EMBASE,Scopus,Web of Science,IEEE Xplore) 和预打印服务器中进行系统搜索.
- 包括初级研究,将重新抽样或成本敏感策略应用于二元临床预测任务,其少数阶级流行率为<30%.
- 描述性合成和随机效应元回归 (逻辑转换AUC) 来分析调节效应 (不平衡比率,策略,模型,样本大小).
主要成果:
- 将编制一个包括临床领域,样本大小,失衡比率,策略,模型类型和性能指标 (AUC) 的综合目录.
- 超回归将量化各种因素对模型性能的影响.
- 分析将使用既有统计方法评估小型研究的影响和稳定性.
结论:
- 确定何时数据层面与算法层面的平衡真正改善了歧视,校准和成本敏感指标.
- 提供基于证据的指导,在医学AI研究中选择处理不平衡的方法.
- 告知报告标准并确定研究缺口,特别是校准和错误分类成本,以便在临床实践中获得可靠的AI.
相关概念视频
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
395
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
395
Strategies for Assessing and Addressing Confounding
349
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...
349
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
Randomized Experiments
8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
8.8K
Bootstrapping
798
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
798
Regression Toward the Mean
6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K


