北京生物医学工程

基于半监督学习的患者相似性度量研究

Analysis of patient similarity measurement based on semi-supervised learning

作者：王妮黄艳群刘红蕾费晓璐魏岚赵相坤陈卉

单位：首都医科大学生物医学工程学院（北京100069）临床生物力学应用基础研究北京市重点实验室（北京100069）首都医科大学宣武医院信息中心（北京100053）

关键词：半监督学习；聚类分析；患者相似性；电子病历；马氏距离

分类号：R318;TP31

出版年·卷·期（页码）：2020·39·2（152-157）

摘要：

目的对数据类型多样的电子病历数据开展基于半监督学习的患者相似性度量研究，评估其可行性和有效性，并为后续个性化研究提供相似患者队列。方法对来自真实世界的电子病历数据，首先特异性计算特征相似性（年龄、性别、疾病、实验室检查），结合专家标注的部分监督信息构成标签集，在标签集中有监督地学习出最优距离度量。然后计算标签集与无标签集数据间的马氏距离，对无标签集中的每个样本，找出与其距离最近的标签集样本，并将其相似性分值作为该无标签样本的患者相似性预测值。最后将学习出的患者相似性作为聚类时评估患者亲疏程度的指标，并与基于传统欧氏距离和余弦距离的聚类结果进行比较。结果较欧氏距离和余弦距离，基于学习出的患者相似性的聚类结果中，患者相似程度更高，聚类效果更好。结论对电子病历数据开展基于半监督学习的患者相似性度量研究是有效的。

Objective To analyze the validity and effect of patient similarity measurement based on semi-supervised learning on electronic medical records and to provide a similar cohort (“patients like me”) for personalized prediction. Methods Based on electronic medical record data, feature similarities (age, sex, disease, laboratory tests) were firstly calculated by using customized measurements. Certain paired feature similarities and their corresponding single similarity score from experts were combined as the label set, based on which the optimal distance measurement was learned by supervised learning. For each sample (i.e., paired similarities for age, sex, disease, and laboratory tests of two patients) of the unlabeled set, its potential similarity score was determined by its nearest neighbor based on the Mahalanobis distance. And then the learned patient similarity was applied to cluster as the closeness degree between patients. The clustering results based on traditional Euclidean distance and cosine distance were given as reference. Results Patients in each cluster based on semi-supervised learning were more similar than those clusters based on classical Euclidian distance and cosine distance. Conclusions It is effective to carry out a study on patient similarity measurement based on semi-supervised learning for electronic medical record data.

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