Awad Aya, Bader-El-Den Mohamed, McNicholas James
1 School of Computing, University of Portsmouth, UK.
2 Queen Alexandra Hospital, Portsmouth Hospitals NHS Trust, UK.
Health Serv Manage Res. 2017 May;30(2):105-120. doi: 10.1177/0951484817696212. Epub 2017 Mar 22.
Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.
在过去几年中，人们对数据挖掘和机器学习方法越来越感兴趣，以提高医院绩效，特别是医院希望通过减少重症监护病房内的死亡患者数量来改善其重症监护病房统计数据。研究集中在可测量结果的预测上，包括并发症风险、死亡率和住院时间。住院时间对于医疗服务提供者和患者来说都是一个重要指标，受到众多因素的影响。特别是，重症监护中的住院时间对于患者体验和护理成本都具有重要意义，并且受到重症监护病房高度复杂环境所特有的因素影响。住院时间经常被用作其他无法测量的结果的替代指标；例如作为医院或重症监护病房死亡率的替代指标。住院时间也是一个参数，已被用于确定疾病的严重程度和医疗资源的利用情况。本文研究了急性医学和重症监护病房中一系列住院时间和死亡率预测应用。它还侧重于分析住院时间和死亡率预测的方法。此外，本文对1984年至2016年期间发表的与生存分析领域相关的一组相关研究论文中涉及的住院时间和死亡率预测分析方法进行了分类和评估。此外，本文还突出了该领域的一些差距和挑战。
