HeteroMed: Heterogeneous Information Network for Medical Diagnosis
Abstract
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.
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With spatial gene expression graphs and corresponding histopathology images, gene expression information is aggregated to generate gene embeddings that can be used to investigate spatial domains such as cancer versus noncancer regions.
“With spatial gene expression graphs (weighted and undirected) and corresponding histopathology images, gene expression information are aggregated to generate embeddings of genes that could then be used to investigate spatial domains (e.g., differentiate between cancer and noncancer regions in tissues) [200].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [200]
A hierarchical knowledge graph was incorporated into learning representations in 6 of the 49 included studies.
“A hierarchical knowledge graph has been incorporated into learning representations in 6 studies [27,31–33,81,91].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [91]
These methods include developing additional components to models for capturing a hierarchical representation from event-level, to visit-level, to patient-level; for encoding the longitudinal factors between clinical events; for incorporating domain knowledge into the representation; and for adopting multiple modalities of EHR data.
“These methods include developing additional components to models for capturing a hierarchical representation from event-level, to visit-level, to patient-level [81,112]; for encoding the longitudinal factors between clinical events [65,77]; for incorporating domain knowledge into the representation [27,31–33,91]; and for adopting multiple modalities of EHR data [11,14,57].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [91]
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- Authors
- Hosseini, A., Chen, T., Wu, W., Sun, Y., & Sarrafzadeh, M.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2018-10-17
- Pages
- 763-772
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W2963622218
- Cited by
- 47
- References
- 32
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 8 · 2021: 14 · 2020: 8 · 2019: 5
- FWCI
- 2.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Funders
- NSF III-1705169 NSF CAREER Award 1741634 (Snapchat gift funds)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, heterogeneous graphs, disease diagnosis, meta-paths, joint embedding, missing data
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