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A weighted similarity measure approach to predict intensive care unit transfers

机译：加权相似性度量方法可预测重症监护病房的转移

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摘要

Classification models have proven useful for predicting clinical interventions and patient outcomes. One of the key issues that affect the predictive ability of supervised learning frameworks in the healthcare scenario is imbalance in data sets. In addition, non-uniform data collection processes in clinical scenarios lead to poor quality data sets. We designed a novel approach to predict Intensive Care Unit (ICU) transfers based on a weighted-similarity measure for patients outside of ICUs. The approach uses similarity between patient vital signs as input features for training the model. To address the data quality issues, we demonstrate the use of various up-sampling and down-sampling techniques to handle imbalanced data sets and train a classifier on a re-sampled data set. The data set used for testing the approach is derived from the MIMIC III database. We compare our results with the clinically accepted methodology to capture patient's health state, assisting in clinical decision making. Our model outperforms the standard methodology used in clinical decision making in standard scoring metrics such as F1-score, False Positive Rate and Mathew's Correlation Coefficient [MCC].
机译：分类模型已被证明对预测临床干预和患者结果有用。在医疗保健场景中影响监督学习框架的预测能力的关键问题之一是数据集的不平衡。另外，在临床情况下，非均匀的数据收集过程会导致数据集的质量较差。我们为重症监护病房以外的患者设计了一种基于加权相似性度量的预测重症监护病房（ICU）转移的新方法。该方法将患者生命体征之间的相似性用作训练模型的输入特征。为了解决数据质量问题，我们演示了使用各种上采样和下采样技术来处理不平衡的数据集并在重采样的数据集上训练分类器。用于测试该方法的数据集来自MIMIC III数据库。我们将结果与临床公认的方法学进行比较，以捕获患者的健康状况，并协助做出临床决策。我们的模型在标准评分指标（例如F1评分，误报率和Mathew相关系数[MCC]）方面优于临床决策中使用的标准方法。

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