Heterogeneous Information Network-Based Patient Similarity Search.
Heterogeneous Information Network-Based Patient Similarity Search.
复制标题
基于异构信息网络的患者相似性搜索
DOI：
10.3389/fcell.2021.735687
复制
发表时间：
2021
影响因子：
5.5
通讯作者：
Wang SP
中科院分区：
文献类型：
--
作者：
Huang HZ;Lu XD;Guo W;Jiang XB;Yan ZM;Wang SP
患者相似度搜索是人工智能辅助医疗服务的基础和重要任务，有助于对相似疾病做出准确预测、推荐个性化治疗方案等医疗诊断。现有的患者相似性搜索方法从电子健康记录（EHR）数据中检索与患者相关的医疗事件，并将其映射到向量。通过计算医疗事件对应向量之间的相似度或不相似度来表示患者之间的相似度，从而完成患者相似度度量。然而，得到的向量往往是高维和稀疏的，这给准确计算患者相似度带来了困难。此外，现有的方法大多不能捕获电子病历中的时间信息，不利于分析时间因素对患者相似度搜索的影响。为了解决这些问题，我们提出了一种基于异构信息网络的患者相似度搜索方法。该方法一方面利用异构信息网络连接患者、疾病和药物，解决了患者、疾病和药物混合信息的矢量表示问题；同时，我们的方法通过计算异构信息网络中节点之间的相似度来度量患者之间的相似度。通过这种方式，可以解决高维和稀疏向量带来的挑战。另一方面，该方法通过将时间信息编码为带注释的异构信息网络，解决了在患者相似度测量过程中由于时间信息使用不足而导致的患者相似度搜索不准确的问题。实验表明，该方法优于对比基线方法。
Patient similarity search is a fundamental and important task in artificial intelligence-assisted medicine service, which is beneficial to medical diagnosis, such as making accurate predictions for similar diseases and recommending personalized treatment plans. Existing patient similarity search methods retrieve medical events associated with patients from Electronic Health Record (EHR) data and map them to vectors. The similarity between patients is expressed by calculating the similarity or dissimilarity between the corresponding vectors of medical events, thereby completing the patient similarity measurement. However, the obtained vectors tend to be high dimensional and sparse, which makes it hard to calculate patient similarity accurately. In addition, most of existing methods cannot capture the time information in the EHR, which is not conducive to analyzing the influence of time factors on patient similarity search. To solve these problems, we propose a patient similarity search method based on a heterogeneous information network. On the one hand, the proposed method uses a heterogeneous information network to connect patients, diseases, and drugs, which solves the problem of vector representation of mixed information related to patients, diseases, and drugs. Meanwhile, our method measures the similarity between patients by calculating the similarity between nodes in the heterogeneous information network. In this way, the challenges caused by high-dimensional and sparse vectors can be addressed. On the other hand, the proposed method solves the problem of inaccurate patient similarity search caused by the lack of use of time information in the patient similarity measurement process by encoding time information into an annotated heterogeneous information network. Experiments show that our method is better than the compared baseline methods.
登录
查看更多内容
影响因子：
3.2
作者：
Sharafoddini A;Dubin JA;Lee J
通讯作者：
Lee J
影响因子：
8.8
作者：
Zhang, Chen;Wang, Guodong;Pan, Ke
通讯作者：
Pan, Ke
影响因子：
17.1
作者：
Li L;Cheng WY;Glicksberg BS;Gottesman O;Tamler R;Chen R;Bottinger EP;Dudley JT
通讯作者：
Dudley JT
影响因子：
9.3
作者：
Gottlieb A;Stein GY;Ruppin E;Altman RB;Sharan R
通讯作者：
Sharan R
HeteSim：异构网络中相关性测量的通用框架
DOI：
10.1109/tkde.2013.2297920
发表时间：
2014-10-01
影响因子：
8.9
作者：
Shi, Chuan;Kong, Xiangnan;Wu, Bin
通讯作者：
Wu, Bin