Machine learning in cardiac surgery: a narrative review

被引:1
作者
Miles, Travis J. [1 ,2 ]
Ghanta, Ravi K. [1 ,2 ]
机构
[1] Baylor Coll Med, Michael E DeBakey Dept Surg, 7200 Cambridge Street Floor 7, Houston, TX 77030 USA
[2] Baylor Coll Med, Dept Surg, Appl Stat & Machine Learning Advancement Surg, Houston, TX 77030 USA
基金
美国国家卫生研究院;
关键词
Cardiac surgery; machine learning (ML); artificial intelligence (AI); critical care; data science; ARTIFICIAL-INTELLIGENCE; PREDICTION; POINTS; MODELS;
D O I
10.21037/jtd-23-1659
中图分类号
R56 [呼吸系及胸部疾病];
学科分类号
摘要
Background and Objective: Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the clinical utility of ML in this domain is unclear. The aim of this review is to provide an overview of ML in cardiac surgery, particularly with regards to its utility in predictive analytics and implications for use in clinical decision support. Methods: We performed a narrative review of relevant articles indexed in PubMed since 2000 using the MeSH terms "Machine Learning", "Supervised Machine Learning", "Deep Learning", or "Artificial Intelligence" and "Cardiovascular Surgery" or "Thoracic Surgery". Key Content and Findings: ML methods have been widely used to generate pre-operat ive risk profiles, consistently resulting in the accurate prediction of clinical outcomes in cardiac surgery. However, improvement in predictive performance over traditional risk metrics has proven modest and current applications in the clinical setting remain limited. Conclusions: Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.
引用
收藏
页码:2644 / 2653
页数:10
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