Machine learning for enhanced healthcare: an overview for operational and clinical leads

L. Roberts, H. Dhanoa, Sadie Lanes, J. Holdship
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引用次数: 1

Abstract

Machine learning has the potential to transform how healthcare is delivered. It can support clinical decision making, determine the risk, presence and prognosis of disease and help optimise patient pathways. Widespread use and access to digital health records mean implementing machine learning models is quicker and easier than ever before. It is imperative for clinical and operational leads to understand the principles behind machine learning, so they can evaluate how it may be helpful to them and their teams. This article provides an overview of machine learning and how it can be used to help solve common healthcare-related problems.
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用于增强医疗保健的机器学习:操作和临床线索概述
机器学习有可能改变医疗保健的提供方式。它可以支持临床决策,确定疾病的风险、存在和预后,并帮助优化患者的路径。数字健康记录的广泛使用和访问意味着实现机器学习模型比以往任何时候都更快、更容易。临床和运营主管必须了解机器学习背后的原理,这样他们才能评估机器学习对他们和他们的团队有何帮助。本文概述了机器学习以及如何使用机器学习来帮助解决常见的医疗保健相关问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
95
期刊介绍: British Journal of Healthcare Management (BJHCM) is the independent monthly journal which is essential reading for all health service managers, policymakers, influencers and commentators. Launched in 1995, BJHCM mixes peer-reviewed management articles with interviews, analysis and comment to bring you a sharp, topical and valuable insight into what"s happening in and around the NHS. To reflect the way that the NHS is changing, the journal has recently received a major face-lift and several new features now appear alongside BJHCM"s excellent state-of-the-art review articles and celebrated columnists.
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