Randall Moorman J
Cardiovascular Division, Department of Internal Medicine, Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, USA.
NPJ Digit Med. 2022 Mar 31;5(1):41. doi: 10.1038/s41746-022-00584-y.
In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record.
2011年,由弗吉尼亚大学牵头的一个多中心团队证明,在新生儿重症监护病房中,使用我们现在所说的人工智能、大数据和机器学习进行实时连续心肺监测可降低死亡率。这项大型随机心率特征试验首次让我们认识到,疾病的早期检测有望实现更早、更有效的干预,并改善患者预后。然而,在随后迅速发展的预测分析监测领域,我们听到的失败案例和成功案例一样多。这篇观点文章旨在描述我们如何开发用于新生儿败血症的心率特征监测,然后将其应用于整个成人重症监护病房和医院医学领域的原理。它主要反映了弗吉尼亚大学团队自20世纪90年代以来的工作:其主题是,在连续心肺监测和电子健康记录中,突然和灾难性的病情恶化之前可能会出现亚临床但可测量的生理变化。