背景：近年来，医学知识和健康数据的数量迅速增加。例如,电子健康记录(EHR)可用性的提高提供了准确的，最新的,并在护理点完成有关患者的信息，使医务人员能够快速访问患者记录，以实现更协调和有效的护理。随着知识的增加，准确的复杂性，循证医学一直在增长。卫生保健工作者必须处理越来越多的数据和文档。同时,相关的患者数据经常被一层不太相关的数据所掩盖，导致医务人员经常错过重要的价值观或异常趋势及其对患者病情进展的重要性。
目的：这项工作的目的是分析重症监护病房（ICU）患者的当前实验室检查结果，并在下一次检查时对这些实验室值进行分类。检测近期的异常情况有助于支持临床医生在ICU的决策过程中，将注意力集中在重要的价值上，并专注于未来的实验室测试。节省他们的时间和金钱。此外,这将给医生更多的时间和病人在一起，而不是略读一长串的实验室值。
方法：我们使用结构化查询语言从MIMIC-III和eICU数据集中提取了ICU中机械通气患者的25个实验室值。此外,我们应用了时间窗口采样和保持，和支持向量机来填充稀疏时间序列中的缺失值，以及Tukey范围来检测和删除异常。然后，我们使用数据训练了4个深度学习模型进行时间序列分类，以及基于梯度提升的算法，并比较了它们在两个数据集上的性能。
结果：在这项工作中测试的模型（深度神经网络和梯度提升），结合预处理管道，在多标签分类任务中实现了至少80%的准确率。此外，基于多个卷积神经网络的模型在两个数据集上都优于其他算法，精度超过89%。
结论：在这项工作中，我们表明，使用机器学习和深度神经网络来预测实验室值的近期异常可以达到令人满意的结果。我们的系统受过训练，已验证，并在2个众所周知的数据集上进行测试，以确保我们的系统尽可能弥合现实差距。最后,该模型可与我们在实际EHR上的预处理管道结合使用,以改善患者的诊断和治疗.
BACKGROUND: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient\'s case.
OBJECTIVE: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.
METHODS: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting-based algorithm and compared their performance on both data sets.
RESULTS: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.
CONCLUSIONS: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients\' diagnosis and treatment.
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