Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. Methods: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. Results: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. Discussion: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

翻译：摘要：引言：机器学习（ML）在高维数据集中识别关键特征，并以达到或超越人类专家水平的精确度执行复杂任务方面取得了巨大成功。方法：我们总结并批判性评估了当前ML在痴呆症研究中的应用，并指出了未来研究方向。结果：我们概述了痴呆症研究中最常用的ML算法，并强调了ML在临床实践、实验医学和临床试验中的未来机遇。我们讨论了可重复性、可复制性和可解释性问题，以及这些问题如何影响痴呆症研究的临床适用性。最后，我们举例说明了如何应用最先进的方法（如迁移学习、多任务学习和强化学习）来克服这些问题，并推动研究成果未来向临床实践的转化。讨论：基于ML的模型在增进我们对痴呆症根本原因和病理机制的理解方面具有巨大潜力。