| 基于多尺度卷积和长短期记忆神经网络的临床危重症疾病预警模型 |
| 投稿时间：2019-10-04 点此下载全文 |
| 引用本文：王天罡,马红叶,蔡宏伟.基于多尺度卷积和长短期记忆神经网络的临床危重症疾病预警模型[J].医学信息学杂志,2020,41(4):41-45 |
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| 基金项目:西安交通大学第一附属医院院基金软科学项目“基于日常临床数据的轻量级数据仓库及检索软件”（项目编号：2018RKX-04）；陕西省社会发展科技公关项目“新药临床试验受试者随机分组及药品管理系统关键技术研究”（项目编号：2016SF-006）。 |
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| 中文摘要:介绍基于多尺度卷积和长短期记忆神经网络的临床危重症疾病预警模型设计与构建,以2 198例危重症患者为研究对象,对模型进行验证,结果表明该模型预测准确率较高,有助于发掘患者数据与病情变化隐藏的关联性,从而辅助医疗决策。 |
| 中文关键词:多尺度卷积神经网络 长短期记忆神经网络 急性肾损伤 疾病预警 |
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| Clinical Critical Disease Early Warning Model Based on Multi-scale Convolution and Long Short Term Memory Neural Network |
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| Abstract:The paper introduces the design and building of clinical critical disease early warning model based on multi-scale Convolution Neural Networks(CNN) and Long Short Term Memory (LSTM) neural network. Taking 2 198 critically ill patients as the object of study, it conducts an experiment towards the model. The results show that the prediction accuracy of the model is higher, which is helpful to explore the hidden correlation between patient data and changes in patients' conditions, so as to offer assistance in medical decision-making. |
| keywords:multi-scale Convolutional Neural Networks(CNN) Long Short Term Memory(LSTM) Acute Kidney Injury(AKI) disease early warning |
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