Stanford University.
University of North Carolina.
Multivariate Behav Res. 2023 Nov-Dec;58(6):1057-1071. doi: 10.1080/00273171.2023.2182753. Epub 2023 May 25.
Despite its potentials benefits, using prediction targets generated based on latent variable (LV) modeling is not a common practice in supervised learning, a dominating framework for developing prediction models. In supervised learning, it is typically assumed that the outcome to be predicted is clear and readily available, and therefore validating outcomes before predicting them is a foreign concept and an unnecessary step. The usual goal of LV modeling is inference, and therefore using it in supervised learning and in the prediction context requires a major conceptual shift. This study lays out methodological adjustments and conceptual shifts necessary for integrating LV modeling into supervised learning. It is shown that such integration is possible by combining the traditions of LV modeling, psychometrics, and supervised learning. In this interdisciplinary learning framework, generating practical outcomes using LV modeling and systematically validating them based on clinical validators are the two main strategies. In the example using the data from the Longitudinal Assessment of Manic Symptoms (LAMS) Study, a large pool of candidate outcomes is generated by flexible LV modeling. It is demonstrated that this exploratory situation can be used as an opportunity to tailor desirable prediction targets taking advantage of contemporary science and clinical insights.
尽管潜在效益显著，但基于潜变量（LV）模型生成的预测目标在监督学习中并不常用，而监督学习是开发预测模型的主要框架。在监督学习中，通常假设要预测的结果是明确且现成的，因此在预测之前验证结果是一个陌生的概念，也是不必要的步骤。LV 建模的通常目的是进行推理，因此将其应用于监督学习和预测环境需要进行重大的概念转变。本研究提出了将 LV 建模集成到监督学习中所需的方法调整和概念转变。结果表明，通过结合 LV 建模、心理测量学和监督学习的传统，可以实现这种集成。在这个跨学科学习框架中，使用 LV 建模生成实用的结果，并根据临床验证者对其进行系统验证，是两种主要策略。在使用纵向躁狂症状评估研究（LAMS）数据的示例中，通过灵活的 LV 建模生成了大量候选结果。结果表明，这种探索性情况可以被利用，以利用当代科学和临床见解来定制理想的预测目标。
