应用机器学习技术和人工智能推进护理点检测。

IF 9.5 1区医学 Q1 CRITICAL CARE MEDICINE

Chest Pub Date : 2025-01-01 Epub Date: 2024-08-23 DOI:10.1016/j.chest.2024.03.051

Craig M Lilly, Apurv V Soni, Denise Dunlap, Nathaniel Hafer, Mary Ann Picard, Bryan Buchholz, David D McManus

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引用次数: 0

摘要

人工智能（AI）的前景在患者、医疗保健专业人员和技术开发人员中引发了热情，他们希望利用人工智能的潜力来加强对越来越多的慢性病和急性病的诊断和管理。床旁检测（POCT）能够在传统医疗环境之外提供医疗服务，因此增加了医疗服务的可及性。开发人员、临床医生和最终用户之间的合作是解决临床问题的有效最佳做法。一套易于研究团队理解、定义明确的通用术语是促进这些合作的宝贵工具。我们简要、准确、清晰地介绍了用于开发新设备和决策支持技术的术语和技术，以及这些术语和技术在 POCT 中的最常见应用。这本用于描述人工智能和机器学习技术的术语词典可为医疗保健专业人士、研究人员、开发人员和患者提供快速参考。常用的方法和技术以表格形式列出，并附有文字说明，介绍其常见用法和所需的数据特征。最后，我们总结了模型有效性测量和组件特征贡献评估。人工智能（AI）是指从数据集中推断意义的非人类技术。它可以进行归纳、分类、预测，并能使用自动学习方法识别关联。本指南概述了这些方法及其在护理点检测中的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Advancing Point-of-Care Testing by Application of Machine Learning Techniques and Artificial Intelligence.

The promise of artificial intelligence has generated enthusiasm among patients, health care professionals, and technology developers who seek to leverage its potential to enhance the diagnosis and management of an increasing number of chronic and acute conditions. Point-of-care testing increases access to care because it enables care outside of traditional medical settings. Collaboration among developers, clinicians, and end users is an effective best practice for solving clinical problems. A common set of clearly defined terms that are easily understood by research teams is a valuable tool that fosters these collaborations.

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来源期刊

Chest 医学-呼吸系统

CiteScore

13.70

自引率

3.10%

发文量

3369

审稿时长

15 days

期刊介绍： At CHEST, our mission is to revolutionize patient care through the collaboration of multidisciplinary clinicians in the fields of pulmonary, critical care, and sleep medicine. We achieve this by publishing cutting-edge clinical research that addresses current challenges and brings forth future advancements. To enhance understanding in a rapidly evolving field, CHEST also features review articles, commentaries, and facilitates discussions on emerging controversies. We place great emphasis on scientific rigor, employing a rigorous peer review process, and ensuring all accepted content is published online within two weeks.