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.

中文翻译：

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基于电子健康记录的图神经网络临床风险预测：一项调查

本研究旨在全面回顾图神经网络（GNN）在基于电子健康记录（EHR）的临床风险预测中的应用。主要目标是概述该主题的最新技术，强调正在进行的研究工作，并确定开发有效的 GNN 以改进临床风险预测方面存在的挑战。在 Scopus、PubMed、ACM Digital Library 和 Embase 数据库中进行了搜索，以识别使用 GNN 基于 EHR 数据进行临床风险预测的相关英文论文。该研究包括 2009 年 1 月至 2023 年 5 月期间发表的原始研究论文。经过初步筛选过程，数据收集中纳入了 50 篇文章。观察到 2020 年以来出版物数量显着增加，大多数选定的论文侧重于诊断预测 (n = 36)。研究表明，图注意力网络（GAT）（n = 19）是最流行的架构，MIMIC-III（n = 23）是最常见的数据资源。 GNN 是通过考虑医疗事件和实体之间的关系以及管理大量 EHR 数据来预测临床风险的相关工具。该领域的未来研究可能会解决 EHR 数据异质性、多模态和模型可解释性等挑战，旨在开发更全面的 GNN 模型，能够产生更准确的预测，在临床环境中有效实施，并最终改善患者护理。