Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long shortterm memory
Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long shortterm memory
复制标题
DOI：
10.1371/journal.pone.0218942
复制
发表时间：
2019-07-08
期刊：
影响因子：
3.7
通讯作者：
Campbell, Roy H.
中科院分区：
文献类型：
--
作者：
Lin, Yu-Wei;Zhou, Yuqian;Campbell, Roy H.
研究背景非计划性再入院是患者面临风险的一个指标，也是一种可避免的医疗资源浪费。除了再入院，重症监护室（ICU）再入院带来了进一步的财务风险，沿着发病率和死亡率风险。识别可能再次入院的高风险患者可以为患者和医疗提供者提供显着的好处。机器学习解决方案的出现，以检测隐藏的模式在复杂的，多维的数据集提供了前所未有的机会，为医生和ICU专家开发一个有效的出院决策支持系统。方法和findingsWe使用监督机器学习方法ICU再入院预测。我们使用机器学习方法对MIMIC-III的综合纵向临床数据进行分析，以预测患者在出院后30天内的ICU再入院率。我们整合了多种类型的功能，包括图表事件，人口统计和ICD-9嵌入。我们已经利用了最近的机器学习技术，例如具有长短期记忆（LSTM）的递归神经网络（RNN），通过这种方法，我们已经能够结合EHR的多变量特征，并捕获图表事件特征（例如葡萄糖和心率）的突然波动。我们表明，我们基于LSTM的解决方案可以更好地捕捉ICU患者的高波动性和不稳定状态，这是ICU再次入院的重要因素。与传统方法相比，我们的机器学习模型识别ICU再入院的灵敏度更高，为0.742（95%CI，0.718-0.766），曲线下面积为0.791（95%CI，0.782-0.800）。我们进行了深入的深度学习性能分析，以及分析每个特征对预测模型的贡献。ConclusionWe的手稿突出了机器学习模型提高ICU决策准确性的能力，是医院精准医疗的真实例子。这些数据驱动的解决方案通过增强医生和ICU专家的临床决策，具有重大临床影响的潜力。我们预计，机器学习模型将改善患者咨询、医院管理、医疗资源分配以及最终的个性化临床护理。
BackgroundUnplanned readmission of a hospitalized patient is an indicator of patients' exposure to risk and an avoidable waste of medical resources. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks. Identification of high-risk patients who are likely to be readmitted can provide significant benefits for both patients and medical providers. The emergence of machine learning solutions to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities for developing an efficient discharge decision-making support system for physicians and ICU specialists.Methods and findingsWe used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We incorporate multiple types of features including chart events, demographic, and ICD-9 embeddings. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able to incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate). We show that our LSTM-based solution can better capture high volatility and unstable status in ICU patients, an important factor in ICU readmission. Our machine learning models identify ICU readmissions at a higher sensitivity rate of 0.742 (95% CI, 0.718-0.766) and an improved Area Under the Curve of 0.791 (95% CI, 0.782-0.800) compared with traditional methods. We perform in-depth deep learning performance analysis, as well as the analysis of each feature contribution to the predictive model.ConclusionOur manuscript highlights the ability of machine learning models to improve our ICU decision-making accuracy and is a real-world example of precision medicine in hospitals. These data-driven solutions hold the potential for substantial clinical impact by augmenting clinical decision-making for physicians and ICU specialists. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.