Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
J Med Internet Res. 2024 Jan 30;26:e50890. doi: 10.2196/50890.
Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.
机器学习（ML）在健康科学研究中取得了令人瞩目的发展，因为它能够处理复杂的数据来执行各种任务，包括无监督学习、监督学习和强化学习。为了帮助健康科学研究人员了解 ML 的优势和局限性，并促进其在研究中的应用，我们在此提出了一个将 ML 整合到分析中的指导方针，通过一个结构化的框架涵盖了从提出研究问题到研究设计和专门数据类型的分析技术的步骤。
