MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, USA; Big Data Analytics Department, Hospital Israelita Albert Einstein, São Paulo, Brazil.
MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, USA; Department of Clinical Data Science Research, Endpoint Health, Inc., USA.
J Crit Care. 2020 Dec;60:64-68. doi: 10.1016/j.jcrc.2020.07.017. Epub 2020 Jul 23.
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simple ordinal severity of illness scores which could be tabulated manually by a human. With the improvements in computing power and proliferation of electronic medical records, entirely new approaches have become possible. Here we review the latest advances in outcome prediction, paying close attention to methods which are widely applicable and provide a high-level overview of the challenges the field currently faces.
在重症监护病房（ICU）中进行准确的预后预测，可以更好地进行治疗规划、调整研究人群的风险，并整体改善患者的护理。过去，预后模型主要关注使用简单的疾病严重程度序数评分来预测死亡率，这些评分可以由人工手动计算。随着计算能力的提高和电子病历的普及，全新的方法成为可能。在这里，我们回顾了预后预测的最新进展，特别关注那些广泛适用且能提供该领域当前面临的挑战的高级概述的方法。
