Giacobbe Daniele Roberto, Signori Alessio, Del Puente Filippo, Mora Sara, Carmisciano Luca, Briano Federica, Vena Antonio, Ball Lorenzo, Robba Chiara, Pelosi Paolo, Giacomini Mauro, Bassetti Matteo
Infectious Diseases Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy.
Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Front Med (Lausanne). 2021 Feb 12;8:617486. doi: 10.3389/fmed.2021.617486. eCollection 2021.
Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.
脓毒症是全球范围内主要的死亡原因。在过去几年中，通过机器学习模型预测临床相关事件受到了特别关注。在本观点中，我们针对在日常实践中使用机器学习预测模型早期检测脓毒症的以下相关方面，提供一个面向临床医生的简要视角：（i）脓毒症定义的争议及其对预测模型开发的影响；（ii）输入特征的选择和可用性；（iii）模型性能的衡量、输出结果及其在临床实践中的实用性。尽管应始终谨慎考虑一些重要的陷阱，但人工智能和机器学习在医疗保健领域的参与度不断提高这一现象不容忽视。从长远来看，采取严谨的多学科方法来丰富我们对机器学习技术在早期识别脓毒症中的应用的理解，可能会在面对这种异质性和复杂性综合征时，显示出增强医疗决策的潜力。
