TY  - CONF
ID  - kulessa21NSS-SPN
T1  - Sum-Product Networks for Early Outbreak Detection of Emerging Diseases
A1  - Kulessa, Moritz
A1  - Wittelsbach, Bennet
A1  - Loza Mencía, Eneldo
A1  - Fürnkranz, Johannes
ED  - Tucker, Allan
ED  - Henriques Abreu, Pedro
ED  - Cardoso, Jaime
ED  - Pereira Rodrigues, Pedro
ED  - Riaño, David
TI  - Artificial Intelligence in Medicine
Y1  - 2021
SP  - 61
EP  - 71
PB  - Springer International Publishing
AD  - Cham
SN  - 978-3-030-77211-6
N2  - 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.
ER  -