HCNN: Heterogeneous Convolutional Neural Networks for Comorbid Risk Prediction with Electronic Health Records
Abstract
The increasing adoption of electronic health record (EHR) systems has brought tremendous opportunities in medicine enabling more personalized prognostic models. However, most work to date has investigated the binary classification problem for predicting the onset of one chronic disease, but little attention has been given to assessing risk of developing comorbidities that are major causes of morbidity and mortality. For example, type 2 diabetes and chronic kidney disease frequently accompany congestive heart failure. This paper is motivated by the problem of predicting comorbid diseases and aims to answer the following question: can we predict the comorbid risk using a patient's medical history? We propose a new predictive learning framework, Heterogeneous Convolutional Neural Network (HCNN), that represents EHRs as graphs with heterogeneous attributes (e.g. diagnoses, procedures, and medication), and then develop a novel deep learning methodology for risk prediction of multiple comorbid diseases. The main innovation of the framework is that it defines the distance between the heterogeneous attributes of the graph representation extracted from the EHR and develops an appropriate learning infrastructure that is a composition of sparse convolutional layers and local pooling steps that match with the local structure of the space of the heterogeneous attributes. As a result, the new method is capable of capturing features about the relationships between heterogeneous attributes of the graphs. Through a comparative study on patient EHR data, HCNN achieves better performance than traditional convolutional neural networks on the risk prediction of comorbid diseases.
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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 [33]
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- Authors
- Zhang, J., Gong, J., & Barnes, L.
- Affiliations
- University of Virginia (US)
- Venue
- 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 214-221
- ISSN
- 2832-2967 (ISSN-L)
- OpenAlex
- W2750342792
- Cited by
- 44
- References
- 36
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 6 · 2022: 5 · 2021: 5 · 2020: 8 · 2019: 4 · 2018: 1 · 2017: 1
- FWCI
- 1.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (publishedVersion)
- 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 › Artificial Intelligence in Healthcare
- Keywords
- electronic health records, type 2 diabetes, chronic kidney disease, congestive heart failure
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