Advancements in graph-based machine learning for electronic health record analysis
Verma, Rohan Mugala, Vamshi Sai Dharmadhikary, Saaksh
Verma, Rohan
Mugala, Vamshi Sai
Dharmadhikary, Saaksh
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Issue Date
2024-11-04
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Conference paper
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Keywords
Class imbalance,EHR data analysis,eICU-CRD dataset,Erroneous entries,Graph Convolutional Networks,Graph-based machine learning,In-hospital mortality prediction,Long Short-Term Memory Networks,Model comparison,Predictive modeling,Reinforcement learning,Self-attention mechanisms
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Citation
R. Verma, V. S. Mugala and S. Dharmadhikary, "Advancements in Graph-Based Machine Learning for Electronic Health Record Analysis," 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 2024, pp. 1-7, doi: 10.1109/ICCCNT61001.2024.10724775.
Abstract
Recent years have witnessed significant progress in applying graph-based machine learning techniques to analyze Electronic Health Record (EHR) data. These methods leverage the interconnected nature of EHR data to extract valuable insights and make predictions at various levels. This paper provides a comprehensive overview of recent advancements in graph-based machine learning approaches for EHR analysis. Specifically, we examine eight models from the literature, comparing their performance on the eICU-CRD dataset. These models encompass a range of techniques, including Graph Convolutional Networks (GCN), Long Short-Term Memory Networks (LSTM), self-attention mechanisms, and reinforcement learning. Additionally, we introduce a novel algorithm designed to address specific challenges posed by EHR data, such as class imbalance and erroneous entries. Our comparative analysis demonstrates the proposed algorithm's effectiveness relative to existing models, highlighting significant improvements in predicting in-hospital mortality. Furthermore, we outline future research directions, including synthesizing existing approaches to develop more generalizable frameworks for EHR diagnosis tasks. This survey provides insights into state-of-the-art methods and potential avenues for advancing graph-based machine learning in EHR analysis. © 2024 IEEE.
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Publisher
Institute of Electrical and Electronics Engineers Inc.
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International Conference on Computing Communication and Networking Technologies, ICCCNT