论文标题
对健康和保健应用的机器学习算法的评估：教程
Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial
论文作者
论文摘要
最近，关于医疗保健中决策支持应用的研究，例如与诊断，预测，治疗计划等相关的研究，最近有了极大的兴趣。这种发展是由于数据可用性的增加以及人工智能和机器学习研究的进步。每天发表高度有希望的研究示例。但是，与此同时，关于医疗保健环境中所需的可靠开发和客观验证的要求，人们的期望存在一些不切实际的期望。这些期望可能会导致最终用户方面未满足的时间表和失望（或非摄取）。本教程的目的是提供有关如何可靠有效地评估性能并避免常见陷阱的实用指导。本教程没有提供DO的清单，而是试图在这些DO的背后建立更好的理解，并且不要介绍最相关的绩效评估标准以及如何计算它们。一路上，我们将指出常见的错误，并提供参考，讨论各种主题更深入。
Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently. This development is thanks to the increase in data availability as well as advances in artificial intelligence and machine learning research. Highly promising research examples are published daily. However, at the same time, there are some unrealistic expectations with regards to the requirements for reliable development and objective validation that is needed in healthcare settings. These expectations may lead to unmet schedules and disappointments (or non-uptake) at the end-user side. It is the aim of this tutorial to provide practical guidance on how to assess performance reliably and efficiently and avoid common traps. Instead of giving a list of do's and don't s, this tutorial tries to build a better understanding behind these do's and don't s and presents both the most relevant performance evaluation criteria as well as how to compute them. Along the way, we will indicate common mistakes and provide references discussing various topics more in-depth.
