Chae Minsu, Han Sangwook, Gil Hyowook, Cho Namjun, Lee Hwamin
Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea.
Department of Internal Medicine, Soonchunhyang University, Cheonan Hospital, Cheonan 31151, Korea.
Diagnostics (Basel). 2021 Jul 13;11(7):1255. doi: 10.3390/diagnostics11071255.
Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM-GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.
心脏骤停可导致严重脑损伤或死亡，因此在心脏骤停发生前进行预测非常重要。然而，包括国家早期预警评分在内的早期预警评分系统存在灵敏度低和误报的问题。我们应用浅层和深度学习来预测心脏骤停以克服这些局限性。我们评估了合成少数过采样技术比率的性能。我们使用决策树、随机森林、逻辑回归、长短期记忆模型、门控循环单元模型和LSTM-GRU混合模型来评估性能。我们提出的逻辑回归显示出比传统早期预警系统更高的阳性预测值和灵敏度。
