使用机器学习技术预测糖尿病患者ICU住院时间

Yuansi Hu, Ling Zheng, Jiacun Wang
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引用次数: 2

摘要

糖尿病是一种常见的慢性疾病,可逐渐对人体各器官系统造成严重损害。重症监护病房(ICU)的糖尿病患者健康状况不佳,需要更多的重症监护和更高的医疗费用。为了促进医院资源管理和改善糖尿病患者的健康结果,在ICU入院的早期阶段准确估计住院时间是必要的。本研究旨在通过将机器学习技术应用于ICU入院前8小时的临床数据,预测糖尿病患者的住院时间。两个预测任务,在ICU的天数和ICU停留时间是长还是短,由阈值10天区分,进行了探讨。神经网络模型预测ICU住院天数效果最佳,R2值为0.3969,平均绝对误差为1.94天。梯度增强模型对ICU长、短住院时间的分类准确率为0.8214。结果表明,这两个模型有希望估计糖尿病患者在ICU入院早期的住院时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting ICU Length of Stay for Patients with Diabetes Using Machine Learning Techniques
Diabetes is a prevalent chronic disease that can result in serious damages to various organ systems gradually. Patients with diabetes in the intensive care unit (ICU) have poor health outcomes and require more intensive care with higher healthcare costs. To facilitate resource management of hospitals and to improve health outcomes of patients with diabetes, accurately estimating the length of stay at an early stage of ICU admissions is necessary. This study is aimed to predict the length of stay for patients with diabetes by applying machine learning techniques on clinical data available during the first 8 hours of ICU admissions. Two prediction tasks, the number of days in ICU and whether an ICU stay is long or short distinguished by the threshold 10 days, were explored. The neural network model achieved the best performance in predicting the number of days in ICU with a R2 value 0.3969 and a mean absolute error 1.94 days. The gradient boosting model is the best one to classify long and short ICU stays with an accuracy 0.8214. The results demonstrate that these two models are promising to estimate the length of stay at an early stage of ICU admissions for patients with diabetes.
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