With the recent availability of Electronic Health Records (EHR) and great
opportunities they offer for advancing medical informatics, there has been
growing interest in mining EHR for improving quality of care. Disease diagnosis
due to its sensitive nature, huge costs of error, and complexity has become an
increasingly important focus of research in past years. Existing studies model
EHR by capturing co-occurrence of clinical events to learn their latent
embeddings. However, relations among clinical events carry various semantics
and contribute differently to disease diagnosis which gives precedence to a
more advanced modeling of heterogeneous data types and relations in EHR data
than existing solutions. To address these issues, we represent how
high-dimensional EHR data and its rich relationships can be suitably translated
into HeteroMed, a heterogeneous information network for robust medical
diagnosis. Our modeling approach allows for straightforward handling of missing
values and heterogeneity of data. HeteroMed exploits metapaths to capture
higher level and semantically important relations contributing to disease
diagnosis. Furthermore, it employs a joint embedding framework to tailor
clinical event representations to the disease diagnosis goal. To the best of
our knowledge, this is the first study to use Heterogeneous Information Network
for modeling clinical data and disease diagnosis. Experimental results of our
study show superior performance of HeteroMed compared to prior methods in
prediction of exact diagnosis codes and general disease cohorts. Moreover,
HeteroMed outperforms baseline models in capturing similarities of clinical
events which are examined qualitatively through case studies.

本研究介绍了一种新的基于异构信息网络 HeteroMed 的疾病诊断建模方法，该方法可以更好地在电子医疗记录中处理缺失值和异构数据，并通过关键诊断路径的捕获和联合嵌入来实现更准确的疾病诊断，其实验结果表明，该方法比现有方法在明确的诊断编码和一般疾病群的预测方面表现更好，并且在通过案例研究定性考察临床事件的相似性方面也优于基准模型。