Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.
Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.
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DOI：
10.3389/fmed.2021.617486
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发表时间：
2021
影响因子：
3.9
通讯作者：
Bassetti M
中科院分区：
文献类型：
--
作者：
Giacobbe DR;Signori A;Del Puente F;Mora S;Carmisciano L;Briano F;Vena A;Ball L;Robba C;Pelosi P;Giacomini M;Bassetti M
脓毒症是世界范围内的主要死亡原因。在过去的几年里，通过机器学习模型预测临床相关事件得到了特别的关注。在目前的观点中，我们提供了关于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的简短的、面向临床医生的观点：（i）脓毒症定义的争议及其对预测模型发展的影响;（ii）输入特征的选择和可用性;（iii）输入特征的选择和可用性;（iv）输入特征的选择和可用性;（iv）输入特征的选择和可用性。（iii）模型性能、输出及其在临床实践中的有用性的测量。人工智能和机器学习越来越多地参与医疗保健，这一点不容忽视，尽管应该始终仔细考虑重要的陷阱。从长远来看，一种严格的多学科方法来丰富我们对机器学习技术应用于脓毒症早期识别的理解，在面对这种异质性和复杂的综合征时，可能会显示出增强医疗决策的潜力。
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.
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影响因子：
3.2
作者：
Desautels T;Calvert J;Hoffman J;Jay M;Kerem Y;Shieh L;Shimabukuro D;Chettipally U;Feldman MD;Barton C;Wales DJ;Das R
通讯作者：
Das R
影响因子：
4.2
作者：
de Miguel, Inigo;Sanz, Begona;Lazcoz, Guillermo
通讯作者：
Lazcoz, Guillermo
影响因子：
7.7
作者：
Calvert, Jacob S.;Price, Daniel A.;Das, Ritankar
通讯作者：
Das, Ritankar
影响因子：
8.8
作者：
Angus, D C;Linde-Zwirble, W T;Pinsky, M R
通讯作者：
Pinsky, M R
影响因子：
8.7
作者：
Delano MJ;Ward PA
通讯作者：
Ward PA