Artificial intelligence holds promise for individualized medicine. Yet, transitioning models from prototyping to clinical applications poses challenges, with confounders being a significant hurdle. We introduce a two-dimensional confounder framework (Confound Continuum), integrating a statistical dimension with a biomedical perspective. Informed and context-sensitive confounder decisions are indispensable for accurate model building, rigorous evaluation and valid interpretation. Using prediction of hand grip strength (HGS) from neuroimaging-derived features in a large sample as an example task, we develop a conceptual framework for confounder considerations and integrate it with an exemplary statistical investigation of 130 candidate confounders. We underline the necessity for conceptual considerations by predicting HGS with varying confound removal scenarios, neuroimaging derived features and machine learning algorithms. We use the confounders alone as features or together with grey matter volume to dissect the contribution of the two signal sources. The conceptual confounder framework distinguishes between high-performance models and pure link models that aim to deepen our understanding of feature-target relationships. The biological attributes of different confounders can overlap to varying degrees with those of the predictive problem space, making the development of pure link models increasingly challenging with greater overlap. The degree of biological overlap allows to sort potential confounders on a conceptual Confound Continuum. This conceptual continuum complements statistical investigations with biomedical domain-knowledge, represented as an orthogonal two-dimensional grid. Exemplary HGS predictions highlighted the substantial impact of confounders on predictive performance. In contrast, choice of features or learning algorithms had considerably smaller influences. Notably, models using confounders as features often outperformed models relying solely on neuroimaging features. Our study provides a confounder framework that combines a statistical perspective on confounders and a biomedical perspective. It stresses the importance of domain expertise in predictive modelling for critical and deliberate interpretation and employment of predictive models in biomedical applications and research.

中文翻译：

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人工智能驱动的精准医疗中混杂因素考虑的框架

人工智能为个体化医疗带来了希望。然而，将模型从原型设计过渡到临床应用面临着挑战，其中混杂因素是一个重大障碍。我们引入了二维混杂因素框架（Confound Continuum），将统计维度与生物医学视角相结合。知情且上下文敏感的混杂因素决策对于准确的模型构建、严格的评估和有效的解释是必不可少的。以大样本中神经影像衍生特征预测握力 (HGS) 作为示例任务，我们开发了一个考虑混杂因素的概念框架，并将其与 130 个候选混杂因素的示例性统计调查相结合。我们通过使用不同的混杂去除场景、神经影像派生特征和机器学习算法来预测 HGS，强调概念性考虑的必要性。我们单独使用混杂因素作为特征或与灰质体积一起使用来剖析两个信号源的贡献。概念混杂因素框架区分了高性能模型和纯链接模型，旨在加深我们对特征-目标关系的理解。不同混杂因素的生物学属性可能与预测问题空间的生物学属性有不同程度的重叠，使得纯链接模型的开发随着重叠程度的增加而变得越来越具有挑战性。生物重叠的程度允许在概念性混淆连续体上对潜在的混杂因素进行排序。这个概念连续体补充了生物医学领域知识的统计调查，表示为正交二维网格。 HGS 预测示例强调了混杂因素对预测性能的重大影响。相比之下，特征或学习算法的选择影响要小得多。值得注意的是，使用混杂因素作为特征的模型通常优于仅依赖神经影像特征的模型。我们的研究提供了一个混杂因素框架，结合了混杂因素的统计视角和生物医学视角。它强调了预测模型领域专业知识对于生物医学应用和研究中预测模型的批判性和深思熟虑的解释和使用的重要性。