Graph neural networks for clinical risk prediction based on electronic health records: A survey

• Published in: Journal of biomedical informatics Vol. 151; p. 104616
• Main Authors: Oss Boll, Heloísa, Amirahmadi, Ali, Ghazani, Mirfarid Musavian, Morais, Wagner Ourique de, Freitas, Edison Pignaton de, Soliman, Amira, Etminani, Farzaneh, Byttner, Stefan, Recamonde-Mendoza, Mariana
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2024
• Subjects: Artificial intelligence; Deep learning; Electronic health records; Graph neural networks; Graph representation learning; IDC; Keyword; CNN; LSTM; Keyword; GNN; GAE; Graph neural networks; GRU; EHR; Deep learning; Graph representation learning; GCN; RNN; GAT; Electronic health records; Artificial intelligence
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2024.104616

This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care. [Display omitted]