Effectively identifying deviations in real-world medical time-series data is a critical endeavor, essential for early surveillance of disease outbreaks. This paper demonstrates the integration of time-series anomaly detection techniques to develop surveillance systems for disease outbreaks. Utilizing data from Sweden's telephone counseling service (1177), we first illustrate the trends in physical and mental symptoms recorded as contact reasons, offering valuable insights for outbreak detection.
View Article and Find Full Text PDFObjective: 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.