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Tutorial: Developing and Deploying Healthcare Predictive Models in R

机译：教程：在r中开发和部署医疗保健预测模型

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Summary form only given. With a rapidly increasing amount of available data and studies proposing novel data analysis methods, researchers often neglect the importance of translational research in healthcare. In translational research, it is important to not only develop and evaluate a novel method, but also to make it useful for practical applications by letting the potential end-user to test it. This tutorial aims to demonstrate the best practices of building, evaluating and deploying predictive models in healthcare using a language and environment for statistical computing called R [1]. According to a recent poll [2] R (61%) is by far the most widely used language for data mining and analytics followed by Python (39%). Despite the popularity of R in core research environment, it is rarely found in production environment mostly due to lack of enterprise level graphical user interface support and historical orientation towards experimental work. However, most of the proposed predictive models presented in healthcare literature do not need a complex graphical interface and could be presented to the potential end-users via a simple web interface. This tutorial will present a framework for developing and deploying predictive models including data preparation, model selection, tuning and evaluation, followed by development of a simple web based graphical user interface to deploy predictive models from R. For all users with at least basic level of R knowledge, we will demonstrate that no additional knowledge is needed to create a simple one-page web application that will increase the impact of their research work by allowing them to publish their predictive models online. It has to be noted that it will be possible to follow the tutorial even if one has never used R. Participants of the tutorial will be able to download the source code of the presented framework and developed application to set up their own web-based predictive model visualizations. This tutorial is in- ended for healthcare professionals and researchers from all fields of healthcare informatics. There will be no specific knowledge needed to follow the tutorial.
机译：仅给出摘要表格。随着迅速增加的可用数据和研究提出新型数据分析方法，研究人员往往忽视了翻译研究在医疗保健方面的重要性。在翻译研究中，不仅要开发和评估一种新方法，还可以通过让潜在的最终用户测试它来实现实际应用。本教程旨在使用称为R [1]的统计计算的语言和环境来展示建筑，评估和部署预测模型的最佳建筑物，评估和部署预测模型。根据最近的民意调查[2] R（61％）是迄今为止最广泛使用的数据挖掘和分析的语言，然后是Python（39％）。尽管核心研究环境中的普及度，但在生产环境中很少被发现，主要是由于缺乏企业级图形用户界面支持和对实验工作的历史方向。然而，医疗文献中呈现的大多数提出的预测模型不需要复杂的图形界面，并且可以通过简单的Web界面呈现给潜在的最终用户。本教程将介绍一个开发和部署预测模型的框架，包括数据准备，模型选择，调整和评估，然后开发一个简单的基于Web的图形用户界面，可以从R部署来自R的预测模型。对于至少具有至少基本级别的所有用户R知识，我们将证明，不需要额外的知识来创建一个简单的单页Web应用程序，这将通过允许它们在线发布其预测模型来提高其研究工作的影响。必须指出的是，即使从未使用过R.参与者，可以遵循教程。教程的参与者将能够下载所提出的框架和开发应用程序的源代码，以建立自己的基于Web的预测性模型可视化。本教程终止了医疗保健专业人士和来自医疗信息管理的所有领域的研究人员。遵循教程将没有具体的知识。

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