Right population, right resources, right algorithm: Using machine learning efficiently and effectively in surgical systems where data are a limited resource
Right population, right resources, right algorithm: Using machine learning efficiently and effectively in surgical systems where data are a limited resource
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DOI：
10.1016/j.surg.2020.11.043
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发表时间：
2021-06-16
期刊：
影响因子：
3.8
通讯作者：
Juillard, Catherine
中科院分区：
文献类型：
--
作者：
Dang, Lauren Eyler;Hubbard, Alan;Juillard, Catherine
人们越来越有兴趣使用机器学习算法来支持低收入和中等收入国家的手术护理、诊断和公共卫生监测。根据我们自己的经验和文献，我们分享了在算法训练和实施所需的数据是有限资源的情况下开发此类模型的几个教训。首先，训练队列应该尽可能与感兴趣的人群相似，当模型被转移到新的环境时，可以使用重新校准来改善风险估计。其次，算法应该整合现有的数据源或前线卫生工作者或助理容易获得的数据，以优化可用资源并促进融入临床实践。第三，超级学习者集成机器学习算法可用于定义给定预测问题的最佳模型，同时最小化算法选择过程中的偏差。通过考虑正确的人口、正确的资源和正确的算法，研究人员可以训练出既适合上下文又有资源意识的预测模型。在数据可用性、负担得起的计算能力和实施研究方面仍然存在差距，这些差距阻碍了临床算法的开发和在低资源环境中的使用，尽管这些障碍随着时间的推移正在减少。我们倡导研究人员创建开源代码、应用程序和培训材料，使新的机器学习模型能够适应不同的人群和背景，以支持全球中低收入国家的手术提供者和医疗保健系统。(c)2020爱思唯尔公司All rights reserved.
There is a growing interest in using machine learning algorithms to support surgical care, diagnostics, and public health surveillance in low-and middle-income countries. From our own experience and the literature, we share several lessons for developing such models in settings where the data necessary for algorithm training and implementation is a limited resource. First, the training cohort should be as similar as possible to the population of interest, and recalibration can be used to improve risk estimates when a model is transported to a new context. Second, algorithms should incorporate existing data sources or data that is easily obtainable by frontline health workers or assistants in order to optimize available resources and facilitate integration into clinical practice. Third, the Super Learner ensemble machine learning algorithm can be used to define the optimal model for a given prediction problem while minimizing bias in the algorithm selection process. By considering the right population, right resources, and right algorithm, researchers can train prediction models that are both context-appropriate and resource-conscious. There remain gaps in data availability, affordable computing capacity, and implementation studies that hinder clinical algorithm development and use in low-resource settings, although these barriers are decreasing over time. We advocate for researchers to create open-source code, apps, and training materials to allow new machine learning models to be adapted to different populations and contexts in order to support surgical providers and health care systems in low-and middle-income countries worldwide. (c) 2020 Elsevier Inc. All rights reserved.