制定和评估时间序列算法,以预测每天的喘住院患者数量
Stephen P Colegate1, Michael Seid2,3,4, David Hartley3,4
1Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, USA.
Journal of clinical and translational science
|September 22, 2025
概括
一个Ensemble模型准确地预测了第二天的儿科喘住院病例,识别了高风险的日子. 这种方法有助于主动的护理管理,通过预测喘入院的激增.
科学领域:
- 儿科医疗保健分析
- 时间序列预测时间序列预测
- 公共卫生信息学 公共卫生信息学
背景情况:
- 喘恶化是儿童住院的主要原因之一.
- 准确预测喘住院情况对于资源配置和患者管理至关重要.
- 现有的预测模型可能无法完全捕捉日常喘入院模式的复杂性.
研究的目的:
- 开发和评估一个时间序列算法,用于预测儿童下一天的每日喘住院病例.
- 为了比较不同的预测模型的性能,包括ARIMA,ETS,Prophet和一个合奏模型.
- 确定喘住院人数超过定义值的高风险日.
主要方法:
- 从2016年1月1日到2023年12月31日收集的每日喘住院数据.
- 评估自回归集成移动平均线 (ARIMA),指数式光滑 (ETS),Prophet和整体模型.
- 在2023年的数据上验证了模型,考虑了各种培训长度和COVID-19流行病前/期间的时期.
主要成果:
- 组合模型在预测高风险日 (6+入院) 中表现出卓越的准确性,正预测值 (PPV) 为0.278,ROC曲线下的面积 (AUC) 为0.740.
- 与单个模型相比,Ensemble模型显示出更好的性能指标,用于识别出现大量喘住院病例的日期.
- 合唱团模型的表现在不同的验证期内都很强,包括受COVID-19大流行影响的人.
结论:
- 组合模型,结合ARIMA,ETS和Prophet的预测,是预测儿科喘住院治疗最有效的方法.
- 将预测分析集成到临床操作中,可以促进积极的预防策略.
- 通过利用准确的住院预测,可以实现加强人口护理管理.
相关概念视频
Asthma-IV: Diagnostic and Management
3.1K
The diagnosis and management of asthma are comprehensive, encompassing clinical assessments, lung function tests, and pharmacological interventions. Here's an overview:
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
3.1K
Asthma-IV: Nursing Management
3.7K
The nursing management of asthma is a comprehensive approach that relies heavily on the expertise and dedication of healthcare professionals. It involves thorough assessment, accurate diagnosis, strategic planning, effective implementation, and diligent evaluation. By meticulously following this step-by-step process, healthcare professionals play a crucial role in providing the best possible care and treatment for patients with asthma, enhancing their overall health and well-being.
First, in...
First, in...
3.7K
Steps in Outbreak Investigation
492
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
492
Asthma-III: Symptoms and Complications
3.3K
Asthma, a common chronic respiratory condition, is classified considering the frequency and severity of symptoms alongside lung function impairment. Understanding this classification is essential for appropriate treatment and management. Here's a detailed look at the classification of asthma and its clinical features and complications:
Classification of Asthma
Classification of Asthma
3.3K
Asthma-I: Introduction
3.4K
Asthma is a chronic respiratory ailment that requires careful management due to its varying symptoms and influencing factors. It is characterized by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, leading to symptoms like wheezing, shortness of breath, chest tightness, and coughing. The symptom frequency and intensity may vary considerably over time. It is also linked to immune system responses to allergens and irritants, highlighting the complex...
3.4K
Asthma-II: Pathophysiology and Classification
4.1K
Asthma is a prevalent chronic respiratory condition marked by inflammation and hyperresponsiveness of the airways. Its pathophysiology involves complex interactions among inflammatory pathways, immune responses, and neural mechanisms.
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
4.1K

