Suppr超能文献

机器学习与健康科学研究:教程。

Machine Learning and Health Science Research: Tutorial.

机构信息

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.

出版信息

J Med Internet Res. 2024 Jan 30;26:e50890. doi: 10.2196/50890.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4530/10865203/288b6e05d5dc/jmir_v26i1e50890_fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验