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相关概念视频

Hospitals-II00:59

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Hospitals provide inpatient and outpatient services. Inpatient services provide care to patients that stay in the hospital for an extended period, ranging from days to months. Examples of inpatient services include intensive care units, hospital wards, or surgeries. Outpatient services provide care to patients who come to a hospital for a diagnostic or treatment but do not stay overnight —for example, diagnostic tests, surgical procedures, or health education.
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Hospitals offer medical and surgical care to the sick and injured, along with accommodation while they recover. At the same time, they also provide outpatient, emergency, psychiatric, and rehabilitation services to meet various community needs. In addition to providing medical care, hospitals also act as hubs for medical research and training. Hospitals use clinical procedures and evidence-based practice standards to deliver patient care. To deliver safe and efficient care, a nurse must stay up...
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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
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The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both...
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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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通过适应性决策树分类器预测COVID-19住院患者的局部激增.

Rachel E Murray-Watson1, Xavier Guaracha2, Alyssa Bilinski3

  • 1School of Public Health, Imperial College London.

Medical decision making : an international journal of the Society for Medical Decision Making
|February 2, 2026
PubMed
概括

决策树分类器有效地使用实时数据预测了COVID-19医院急剧增长,优于现有指标. 这些模型为流行病期间的医疗保健能力风险提供了可解释的见解.

关键词:
在 COVID-19 疫情中,决策树是一个决策树.逻辑回归的逻辑回归机器学习是机器学习.神经网络的神经网络的神经网络

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科学领域:

  • 公共卫生监督 公共卫生监督
  • 流行病学 流行病学
  • 医疗保健中的机器学习

背景情况:

  • 随着COVID-19大流行,医疗保健系统受到压力,需要准确预测医院容量风险.
  • 现有的风险指标,如CDC的社区水平,在实时更新和直接结果预测方面存在局限性.
  • 一些模型中缺乏可解释性阻碍了减轻医院急剧增长的决策.

研究的目的:

  • 评估适应性决策树分类器,以预测COVID-19住院病例的激增.
  • 将决策树分类器的性能与后勤回归,神经网络和CDC社区级别进行比较.
  • 评估公共卫生决策决策树模型的可解释性和实时更新能力.

主要方法:

  • 开发了实时决策树分类器,以预测从2020年7月到2022年11月的COVID-19住院急剧增加.
  • 使用诸如接收器运行特征曲线 (auROC) 下的面积和精度回忆曲线 (auPRC) 下的面积等指标来评估性能.
  • 与后勤回归,神经网络模型和CDC的社区级别进行了比较.

主要成果:

  • 决策树分类器在大多数周实现了auROC>80%,在预测高医院占用率方面超过了CDC的社区水平.
  • 随着时间的推移,auPRC,灵敏度和特异性在20%-100%之间变化,与大流行浪潮保持一致.
  • 决策树模型的性能与逻辑回归和神经网络相比较,具有更高的解释性.

结论:

  • 常规收集的医院监视数据可用于自适应地更新决策树分类器,用于预测住院急剧增加.
  • 决策树分类器为理解和应对流行病变化提供了可解释的规则.
  • 这些分类器的灵敏度和特异性可以在不同的流行病浪潮中显著波动.