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

Oss Boll, Heloísa (författare)
Högskolan i Halmstad,Akademin för informationsteknologi,Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Amirahmadi, Ali, 1994- (författare)
Högskolan i Halmstad,Akademin för informationsteknologi
Ghazani, Mirfarid Musavian, 1989- (författare)
Högskolan i Halmstad,Akademin för informationsteknologi
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Ourique de Morais, Wagner, 1979- (författare)
Högskolan i Halmstad,Akademin för informationsteknologi
Pignaton de Freitas, Edison, 1979- (författare)
Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Soliman, Amira, 1980- (författare)
Högskolan i Halmstad,Akademin för informationsteknologi
Etminani, Farzaneh, 1984- (författare)
Högskolan i Halmstad,Akademin för informationsteknologi
Byttner, Stefan, 1975- (författare)
Högskolan i Halmstad,Akademin för informationsteknologi
Recamonde-Mendoza, Mariana (författare)
Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
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 (creator_code:org_t)
Maryland Heights, MO : Academic Press, 2024
2024
Engelska.
Ingår i: Journal of Biomedical Informatics. - Maryland Heights, MO : Academic Press. - 1532-0464 .- 1532-0480. ; 151
  • Forskningsöversikt (refereegranskat)
Abstract Ämnesord
Stäng  
  • Objective: 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. Methods: 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. Results: 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. Conclusion: 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. © 2024 The Authors

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Artificial intelligence
Deep learning
Electronic health records
Graph neural networks
Graph representation learning
Keyword
IDC
IDC
IDC
IDC

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