Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Interventional Cardiology and Cardiovascular Medicine Research, Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland.
J Evid Based Med. 2020 Nov;13(4):301-312. doi: 10.1111/jebm.12418. Epub 2020 Nov 13.
Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.
人工智能（AI）在大数据时代已经进入临床研究。急性呼吸窘迫综合征（ARDS）或急性肺损伤（ALI）是一种包含异质人群的临床综合征。管理这种异质患者群体对临床医生来说是一个巨大的挑战。随着越来越多的 ALI 数据集公开可用，通过复杂的分析可以发现更多的知识。我们综述了利用大数据分析的文献，以了解 AI 在改善 ALI/ARDS 患者护理方面的作用。许多研究利用电子病历（EMR）数据来识别和预测 ARDS 患者。随着越来越多的 ARDS 临床试验数据向公众开放，对这些组合数据集进行二次分析为从新的角度解决临床问题提供了一种强有力的方法。分类和回归树（CART）和人工神经网络（ANN）等 AI 技术也已成功用于 ARDS 问题的研究。通过 AI 的支持，可以实现 ARDS 的个体化治疗，因为我们现在可以通过无监督机器学习算法将 ARDS 分为许多亚表型。有趣的是，这些亚表型对某种干预的反应不同。然而，目前涉及 ARDS 的分析尚未充分纳入转录组、蛋白质组学、日常活动和环境条件等组学信息。AI 技术正在帮助我们解释 ARDS 患者的复杂数据，并使我们能够在未来通过个体化治疗方案进一步改善 ARDS 患者的管理。
