Rajendran Suraj, Pan Weishen, Sabuncu Mert R, Chen Yong, Zhou Jiayu, Wang Fei
Tri-Institutional Computational Biology & Medicine Program, Cornell University, Ithaca, NY, USA.
Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Patterns (N Y). 2024 Jan 17;5(2):100913. doi: 10.1016/j.patter.2023.100913. eCollection 2024 Feb 9.
In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. We argue that C approaches could pave the way for ML models that are both holistic and widely applicable. This paper provides a comprehensive overview of C in health care, including its present stage, potential opportunities, and associated challenges.
在医疗保健领域，机器学习（ML）在增强患者护理、改善人群健康状况以及简化医疗工作流程方面展现出巨大潜力。然而，对数据隐私、数据源多样性以及不同数据模式利用不足的担忧，常常阻碍其充分发挥潜力。本综述研究了跨队列跨类别（C）整合在此类背景下的效用：即从分布在不同安全站点的多样数据集中组合信息的过程。我们认为，C方法可为兼具整体性和广泛适用性的ML模型铺平道路。本文全面概述了医疗保健领域的C，包括其当前阶段、潜在机遇及相关挑战。
