基于ICD-10诊断编码的慢性病并发症聚类算法

Chronic disease complications clustering based on ICD-10 diagnoses code

慢性病与其相关并发症关系的研究,对患者以及医学研究都有重要意义。电子病历中记录的患者就诊数据为研究目标慢性病与其并发症的关系提供了数据基础,其中面临的挑战之一在于既需要使用临床医生的领域知识对并发症进行标注,又不希望给医生增加过多负担。设计了一种采用分组策略的基于ICD-10诊断编码的慢性病并发症半监督聚类方法,以实现在较少的医生专家参与下对慢性病并发症归类。真实糖尿病患者电子医疗记录数据集上的实验结果表明提出的算法是实用且有效的。

Study on the relationship between the chronic disease and the corresponding complications has great theoretical significance and applicable value for patients and clinical medicine. In order to utilize healthcare electronic record more reasonably, preprocessing was needed according to prior medical knowledge for chronic disease complication. The challenge of this work is that medical knowledge should be exploited to label the corresponding complications. To meet these challenges and assist physicians in labeling complications of a target chronic disease, a semi-supervised chronic disease complications clustering algorithm based on ICD-10 code for diagnoses was proposed. Experiments on a real dataset of diabetes electronic healthcare record show that the algorithms are practical and effective.