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Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records

机译：异构电子病历中半监督学习患者相似性研究

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A new learning-based patient similarity measurement was proposed to measure patients’ similarity for heterogeneous electronic medical records (EMRs) data. We first calculated feature-level similarities according to the features’ attributes. A domain expert provided patient similarity scores of 30 randomly selected patients. These similarity scores and feature-level similarities for 30 patients comprised the labeled sample set, which was used for the semi-supervised learning algorithm to learn the patient-level similarities for all patients. Then we used the k-nearest neighbor (kNN) classifier to predict four liver conditions. The predictive performances were compared in four different situations. We also compared the performances between personalized kNN models and other machine learning models. We assessed the predictive performances by the area under the receiver operating characteristic curve (AUC), F1-score, and cross-entropy (CE) loss. As the size of the random training samples increased, the kNN models using the learned patient similarity to select near neighbors consistently outperformed those using the Euclidean distance to select near neighbors (all P values??0.001). The kNN models using the learned patient similarity to identify the top k nearest neighbors from the random training samples also had a higher best-performance (AUC: 0.95 vs. 0.89, F1-score: 0.84 vs. 0.67, and CE loss: 1.22 vs. 1.82) than those using the Euclidean distance. As the size of the similar training samples increased, which composed the most similar samples determined by the learned patient similarity, the performance of kNN models using the simple Euclidean distance to select the near neighbors degraded gradually. When exchanging the role of the Euclidean distance, and the learned patient similarity in selecting the near neighbors and similar training samples, the performance of the kNN models gradually increased. These two kinds of kNN models had the same best-performance of AUC 0.95, F1-score 0.84, and CE loss 1.22. Among the four reference models, the highest AUC and F1-score were 0.94 and 0.80, separately, which were both lower than those for the simple and similarity-based kNN models. This learning-based method opened an opportunity for similarity measurement based on heterogeneous EMR data and supported the secondary use of EMR data.
机译：提出了一种新的基于学习的患者相似性测量来衡量异构电子医疗记录（EMRS）数据的患者的相似性。我们首先根据功能的属性计算特征级相似度。域名专家提供了30名随机选择的患者的患者相似性评分。这些相似性分数和30名患者的特征级别相似之处包括标记的样本集，用于半监督的学习算法来学习所有患者的患者水平相似之处。然后我们使用K-Collect邻居（KNN）分类器来预测四个肝脏条件。在四种不同的情况下比较预测性表演。我们还将个性化KNN模型与其他机器学习模型之间的性能进行了比较。我们评估了接收器操作特征曲线（AUC），F1分数和交叉熵（CE）损耗的区域的预测性能。随着随机训练样本的尺寸增加，KNN模型使用所学的患者相似性来选择近邻的选择始终优于使用欧几里德距离选择近邻的那些（所有P值？<0.001）。来自随机训练样本的学习患者相似性的KNN模型也具有较高的最佳性能（AUC：0.95对0.89，F1分数：0.84与0.67，CE损失：1.22 VS 。1.82）比使用欧几里德距离的人。随着类似训练样本的大小增加，这组成了由学习患者相似性确定的最相似的样本，因此使用简单的欧几里德距离选择KNN模型的性能，以逐渐降低近邻。在交换欧几里德距离的作用时，以及在选择近邻和类似的训练样本方面学习的患者相似性时，KNN模型的性能逐渐增加。这两种KNN模型具有相同的AUC 0.95，F1分数0.84和CE损失1.22。在四个参考模型中，最高的AUC和F1分数分别为0.94和0.80，它们均低于基于简单和相似性的KNN模型。基于学习的方法为基于异构EMR数据开辟了相似性测量的机会，并支持EMR数据的二次使用。

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