Machine learning and algorithmic fairness in public and population health
Machine learning and algorithmic fairness in public and population health
复制标题
机器学习和算法公平性在公共和人口健康中的应用
DOI：
10.1038/s42256-021-00373-4
复制
发表时间：
2021-07-29
影响因子：
23.8
通讯作者：
Chunara, Rumi
中科院分区：
文献类型：
--
作者：
Mhasawade, Vishwali;Zhao, Yuan;Chunara, Rumi
到目前为止，机器学习和健康方面的大部分工作都集中在医院或诊所内部的流程上。然而，这只是与健康相关的一组狭窄的任务和挑战;通过在更广泛的健康任务中利用机器学习，有更大的影响潜力。在这一视角中，我们的目标是在健康及其影响的整体视角下强调机器学习的潜在机遇和挑战。为此，我们建立在人口和公共卫生研究的基础上，重点是不同文化，社会和环境因素之间的机制及其对个人和社区健康的影响。我们简要介绍了这些领域的研究，数据源和任务类型，并使用这些来确定机器学习相关的设置，并可以为新知识做出贡献。鉴于健康公平的关键焦点以及公共和人口健康的差异，我们将这些主题与机器学习算法公平子领域并列，以突出机器学习，公共和人口健康可以协同实现健康公平的具体机会。改善治疗的药物解决方案正在开始改变医疗保健。Mhasawade及其同事在这个视角中讨论了机器学习在人口和公共卫生领域的应用如何超越临床实践。虽然处理一般健康数据也有其自身的挑战，最明显的是在现有的健康差异面前确保算法的公平性，但该领域为机器学习社区提供了新的数据和问题。
Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity.Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.