预测类风湿关节炎患者的到来,包括气象因素和空气污染物
Zhe Ye1, Benjun Ye2, Zilin Ming3
1Department of Endocrinology, Hangzhou Linping Traditional Chinese Medicine Hospital, No. 101 Yuncheng Street, Linping District, Hangzhou City, Zhejiang Province, China.
Scientific reports
|August 1, 2024
概括
使用深度学习模型预测类风湿性关节炎患者的到来,提高了准确性. 纳入环境数据进一步提高了对更好的医疗资源管理的预测.
科学领域:
- * 医学信息学和人工智能.
- * 公共卫生和流行病学.
- *环境健康和气象学.
背景情况:
- * 风湿性关节炎 (RA) 负担的增加需要改善医疗资源的分配.
- *准确预测RA患者的到来对于高效的医疗管理至关重要.
- *现有的预测方法没有考虑环境因素.
研究的目的:
- * 开发和评估一个长期短期记忆 (LSTM) 深度学习模型,用于预测RA患者的到来.
- * 将LSTM模型的性能与传统移动平均 (MA) 和自动回归 (AR) 模型进行比较.
- * 评估气象因素和空气污染物对预测准确性的影响.
主要方法:
- *从2015年到2022年收集和规范化RA患者,气象和空气污染物数据.
- *构建基于MA和AR的模型,以及单变量和多变量LSTM模型.
- *使用根平均平方误差 (RMSE) 对不同训练测试集比率的模型性能评估.
主要成果:
- * LSTM模型在所有数据集大小中显示出与基于MA和AR的模型相比,更高的预测准确度.
- *包括气象因素和空气污染物显著提高了LSTM模型的预测性能.
- *多变量LSTM模型实现了较低的RMSE值,表明预测精度提高.
结论:
- *LSTM深度学习模型为预测RA患者的到来提供了更准确的方法.
- *将环境数据集成到LSTM模型中,进一步提升了预测准确度.
- *这种新的预测方法支持优化医疗资源管理,并解决资源短缺问题.
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