Predictive modeling in neurocritical care using causal artificial intelligence.
Predictive modeling in neurocritical care using causal artificial intelligence.
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DOI：
10.5492/wjccm.v10.i4.112
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发表时间：
2021-07-09
期刊：
影响因子：
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通讯作者：
Rabinstein AA
中科院分区：
文献类型：
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作者：
Dang J;Lal A;Flurin L;James A;Gajic O;Rabinstein AA
人工智能（AI）和各种系统的数字孪生模型长期以来一直用于工业中，以快速有效地测试产品。数字双胞胎在临床医学中的使用在2003年阿基米德糖尿病人工智能模型的开发中引起了人们的注意。最近，人工智能模型已被应用于心脏病学，内分泌学和本科医学教育领域。到目前为止，数字孪生和人工智能的使用主要集中在慢性疾病管理上，它们在重症监护医学领域的应用仍然很少探索。在神经重症监护中，目前的人工智能技术专注于解释脑电图，监测颅内压和预测结果。人工智能模型已经被开发出来，通过帮助注释描记、检测癫痫发作和识别无反应患者的大脑激活来解释脑电图。在这篇小型综述中，我们描述了构建与神经重症监护相关的可操作AI模型的挑战和机遇，该模型可用于教育新一代临床医生并增强临床决策。
Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.