[BibTeX] [RIS]
Sum-Product Networks for Early Outbreak Detection of Emerging Diseases
Type of publication: Inproceedings
Citation: kulessa21NSS-SPN
Booktitle: Artificial Intelligence in Medicine
Year: 2021
Pages: 61--71
Publisher: Springer International Publishing
Address: Cham
ISBN: 978-3-030-77211-6
Abstract: Recent research in syndromic surveillance has focused primarily on monitoring specific, known diseases, concentrating on a certain clinical picture under surveillance. Outbreaks of emerging infectious diseases with different symptom patterns are likely to be missed by such a surveillance system. In contrast, monitoring all available data for anomalies allows to detect any kind of outbreaks, including infectious diseases with yet unknown syndromic clinical pictures. In this work, we propose to model the joint probability distribution of syndromic data with sum-product networks (SPN), which are able to capture correlations in the monitored data and even allow to consider environmental factors, such as the current influenza infection rate. Conversely to the conventional use of SPNs, we present a new approach to detect anomalies by evaluating p-values on the learned model. Our experiments on synthetic and real data with synthetic outbreaks show that SPNs are able to improve upon state-of-the-art techniques for detecting outbreaks of emerging diseases.
Keywords:
Authors Kulessa, Moritz
Wittelsbach, Bennet
Loza Mencía, Eneldo
Fürnkranz, Johannes
Editors Tucker, Allan
Henriques Abreu, Pedro
Cardoso, Jaime
Pereira Rodrigues, Pedro
Riaño, David
Topics