HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911816054996992 |
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| author | Piya, Fahmida Liza Gupta, Mehak Beheshti, Rahmatollah |
| author_facet | Piya, Fahmida Liza Gupta, Mehak Beheshti, Rahmatollah |
| contents | While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly limit the performance or even applicability of downstream tasks using EHRs. To address this challenge, we present HealthGAT, a novel graph attention network framework that utilizes a hierarchical approach to generate embeddings from EHR, surpassing traditional graph-based methods. Our model iteratively refines the embeddings for medical codes, resulting in improved EHR data analysis. We also introduce customized EHR-centric auxiliary pre-training tasks to leverage the rich medical knowledge embedded within the data. This approach provides a comprehensive analysis of complex medical relationships and offers significant advancement over standard data representation techniques. HealthGAT has demonstrated its effectiveness in various healthcare scenarios through comprehensive evaluations against established methodologies. Specifically, our model shows outstanding performance in node classification and downstream tasks such as predicting readmissions and diagnosis classifications.
Our code is available at https://github.com/healthylaife/HealthGAT |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks Piya, Fahmida Liza Gupta, Mehak Beheshti, Rahmatollah Machine Learning Computers and Society While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly limit the performance or even applicability of downstream tasks using EHRs. To address this challenge, we present HealthGAT, a novel graph attention network framework that utilizes a hierarchical approach to generate embeddings from EHR, surpassing traditional graph-based methods. Our model iteratively refines the embeddings for medical codes, resulting in improved EHR data analysis. We also introduce customized EHR-centric auxiliary pre-training tasks to leverage the rich medical knowledge embedded within the data. This approach provides a comprehensive analysis of complex medical relationships and offers significant advancement over standard data representation techniques. HealthGAT has demonstrated its effectiveness in various healthcare scenarios through comprehensive evaluations against established methodologies. Specifically, our model shows outstanding performance in node classification and downstream tasks such as predicting readmissions and diagnosis classifications. Our code is available at https://github.com/healthylaife/HealthGAT |
| title | HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2403.18128 |