Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data.

IF 2.6 4区 工程技术 Q1 Mathematics
Shu Wei Chou-Chen, Luis A Barboza
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引用次数: 0

Abstract

Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

利用气候和污染数据预测哥斯达黎加呼吸系统疾病的出院人数。
呼吸系统疾病是全球医疗系统最沉重的经济负担之一。病例增加的变化在很大程度上取决于气候的季节性影响、社会经济因素和污染。因此,了解这些变化并获得精确的预测,可以让卫生当局在分配有限的经济和人力资源时做出正确的决策。我们的目标是利用四种统计学习技术(随机森林、XGboost、Facebook 的先知预测模型和结合上述方法的集合方法),以及 22 个气候变化指数和作为污染指标的气溶胶光学深度,对哥斯达黎加七个地区医院每周因呼吸道疾病住院的人数进行建模和预测。我们使用 2000 年至 2018 年的数据对模型进行了训练,并使用 2019 年的数据作为测试数据对模型进行了评估。在训练期间,我们设置了 2 年的滑动窗口和 1 年的评估期,并采用网格搜索法优化每个模型的超参数。根据预测精度和防止过度拟合的原则,使用测试数据为每个区域选出最佳模型。然后利用保角推理计算预测区间。针对最佳模型计算了所有气候变量的相对重要性,并根据所选模型观察了七个地区中某些地区的类似模式。最后,七个地区的医院都获得了可靠的预测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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