改善糖尿病血糖控制的人工智能和机器学习:最佳实践、陷阱和机遇。

IF 17.2 1区 工程技术 Q1 ENGINEERING, BIOMEDICAL
Peter G. Jacobs;Pau Herrero;Andrea Facchinetti;Josep Vehi;Boris Kovatchev;Marc D. Breton;Ali Cinar;Konstantina S. Nikita;Francis J. Doyle;Jorge Bondia;Tadej Battelino;Jessica R. Castle;Konstantia Zarkogianni;Rahul Narayan;Clara Mosquera-Lopez
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引用次数: 0

摘要

目的:人工智能和机器学习正在改变包括医学在内的许多领域。在糖尿病方面,强大的生物传感技术和自动胰岛素输送疗法为改善健康创造了巨大的机会。尽管近年来涉及将机器学习应用于糖尿病主题的手稿数量有所增加,但用于训练和评估这些算法的方法、指标和数据缺乏一致性。这份手稿为糖尿病领域的机器学习从业者提供了一致的指导方针,包括最佳实践推荐方法和避免陷阱的警告。方法:回顾了算法方法,并讨论了不同算法的优点,包括临床准确性、可解释性、可解释和个性化的重要性。我们回顾了糖尿病血糖控制中机器学习应用中最常见的功能,并提供了一个用于计算功能的开源函数库,以及一个使用数据表指定数据集的框架。提供了对可用于训练算法的当前数据集的审查,以及数据源的在线存储库。意义:这些共识指南旨在提高工程师和数据科学家在糖尿病领域开发的新机器学习算法的性能和可翻译性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities
Objective: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. Methods: Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. Significance: These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
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来源期刊
IEEE Reviews in Biomedical Engineering
IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering
CiteScore
31.70
自引率
0.60%
发文量
93
期刊介绍: IEEE Reviews in Biomedical Engineering (RBME) serves as a platform to review the state-of-the-art and trends in the interdisciplinary field of biomedical engineering, which encompasses engineering, life sciences, and medicine. The journal aims to consolidate research and reviews for members of all IEEE societies interested in biomedical engineering. Recognizing the demand for comprehensive reviews among authors of various IEEE journals, RBME addresses this need by receiving, reviewing, and publishing scholarly works under one umbrella. It covers a broad spectrum, from historical to modern developments in biomedical engineering and the integration of technologies from various IEEE societies into the life sciences and medicine.
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