用RNN提升重症患者风险预测准确率，助力医院资源优化
Recurrent Neural Network on PICTURE Model
- 基于RNN构建时序模型捕捉患者生理数据动态变化
- 在PICTURE数据集上表现优于XGBoost模型，提升预测准确率
- 适合医疗决策支持系统开发者及临床研究者参考
重症监护室（ICU）为医院中最危重患者提供关键救治。随着ICU需求急剧增长，尤其是在新冠疫情背景下，精准识别最危重患者有助于医院更高效分配资源并挽救更多生命。预测重症监护转移及其他突发事件（PICTURE）模型通过区分高风险与低风险患者来预测患者恶化情况。本研究旨在实现一种深度学习模型，以基准测试现有表现优异的XGBoost模型的预测性能。
原文摘要 · Abstract (English)
Intensive Care Units (ICUs) provide critical care and life support for most severely ill and injured patients in the hospital. With the need for ICUs growing rapidly and unprecedentedly, especially during COVID-19, accurately identifying the most critical patients helps hospitals to allocate resources more efficiently and save more lives. The Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE) model predicts patient deterioration by separating those at high risk for imminent intensive care unit transfer, respiratory failure, or death from those at lower risk. This study aims to implement a deep learning model to benchmark the performance from the XGBoost model, an existing model which has competitive results on prediction.
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