A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS. Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.

中文翻译：

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医疗保健中的机器学习——系统视角

碎片化和孤立的医疗保健格局的一个后果是，患者护理（和数据）分散在众多不同的设施和计算机系统中，并且很难在这些系统之间实现互操作性。缺乏互操作性不仅阻碍了护理的连续性和负担提供者，而且阻碍了机器学习 (ML) 算法的有效应用。因此，目前大多数旨在了解患者护理和促进临床决策支持的 ML 算法都是在有限的数据集上进行训练的。这种方法类似于还原论的牛顿范式，在这种范式中，系统被分解为基本组件，并通过对这些组件的单独理解来形成对整体的描述。还原论方法的一个关键限制是它忽略了系统内组件-组件的相互作用和动力学，这对于理解复杂自适应系统 (CAS) 的整体行为通常具有重要意义。医疗保健是一个CAS。尽管机器学习在健康数据上的应用已经显示出对临床决策支持的逐步改进，但机器学习在重组整体护理服务和最大化护理价值方面具有更广泛的潜力。然而，这种 ML 潜力在很大程度上仍未开发：主要是由于电子健康记录 (EHR) 的功能限制以及无法将医疗保健系统视为一个整体。这种观点 (i) 将医疗保健阐明为一个具有生物学和组织视角的复杂系统，(ii) 以示例激发系统的需求”