人工智能时代的高血压诊断与管理变革:美国国家心肺血液研究所(NHLBI)2023 年研讨会报告。

IF 6.9 1区 医学 Q1 PERIPHERAL VASCULAR DISEASE Hypertension Pub Date : 2024-07-16 DOI:10.1161/HYPERTENSIONAHA.124.22095
Daichi Shimbo, Rashmee U Shah, Marwah Abdalla, Ritu Agarwal, Faraz S Ahmad, Gabriel Anaya, Zachi I Attia, Sheana Bull, Alexander R Chang, Yvonne Commodore-Mensah, Keith Ferdinand, Kensaku Kawamoto, Rohan Khera, Jane Leopold, James Luo, Sonya Makhni, Bobak J Mortazavi, Young S Oh, Lucia C Savage, Erica S Spatz, George Stergiou, Mintu P Turakhia, Paul K Whelton, Clyde W Yancy, Erin Iturriaga
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引用次数: 0

摘要

高血压是心血管疾病、慢性肾病和痴呆症最重要的风险因素之一。人工智能(AI)领域发展迅速,但关于如何利用人工智能改善高血压诊断和管理的讨论却很少。包括机器学习工具在内的人工智能技术可以改变我们诊断和管理高血压的方式,并对改善个人和群体健康产生潜在影响。在公共卫生和医疗保健系统中开发成功的人工智能工具需要不同类型的专业知识,以及临床医生、工程师和数据科学家之间的合作关系。公正的数据来源、管理和分析仍然是一项基本挑战。从诊断的角度来看,机器学习工具可以改进血压测量,并有助于预测高血压的发病率。为了推进高血压的管理,机器学习工具可能有助于通过分析预测患者对降压药物的反应和高血压相关并发症的风险,为患者找到个性化的治疗方法。然而,将人工智能工具用于高血压治疗在现实世界中还存在一些挑战。在此,我们总结了参加美国国家心肺血液研究所于 2023 年 3 月举办的研讨会的不同利益相关者的主要发现。研讨会与会者介绍了以下方面的信息:医疗保健中临床医学、数据科学和工程学之间的沟通差距;估测血压、高血压风险和血压控制的新方法;以及现实世界中的实施挑战和问题。
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Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

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来源期刊
Hypertension
Hypertension 医学-外周血管病
CiteScore
15.90
自引率
4.80%
发文量
1006
审稿时长
1 months
期刊介绍: Hypertension presents top-tier articles on high blood pressure in each monthly release. These articles delve into basic science, clinical treatment, and prevention of hypertension and associated cardiovascular, metabolic, and renal conditions. Renowned for their lasting significance, these papers contribute to advancing our understanding and management of hypertension-related issues.
期刊最新文献
Evolutionary Characteristics in Primary Aldosteronism Patients. Rhythmic Contractions of Lymph Vessels and Lymph Flow Are Disrupted in Hypertensive Rats. Hyperadrenergic Postural Tachycardia Syndrome: Clinical Biomarkers and Response to Guanfacine. Predictive and Diagnostic Value of the Angiogenic Proteins in Patients With Chronic Kidney Disease. GPER Stimulation Attenuates Cardiac Dysfunction in a Rat Model of Preeclampsia.
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