ABSTRACT
The Data Atlas is the centerpiece of the PERISCOPE project’s data-driven research. The Atlas constitutes a centralized access point for the exploration, visualization and analysis of the original data produced by PERISCOPE partners, integrated with the most relevant information about the COVID-19 pandemic and its effects on health, economics, policy-making, and society at large. The Atlas interfaces and tools make such data readily available to the research community, decision makers and the general public, providing the means to amplify its reach and impact. The present demo, showcases the features of v1.2 release of the Atlas, 18 months from the project kick-off, and some of the planned enhancements to be delivered until project month 24. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The ongoing pandemics of coronavirus disease has accelerated the implementation of machine learning methods (ML) to support clinical decisions. Within this context, we present the ALFABETO project, whose aim is to aid clinicians during COVID-19 patients hospital admission through the application of ML approaches exploiting clinical and chest x-ray features. Yet, non linear ML classifiers are often perceived as not easily interpretable by users, thus hampering trust in ML predictions. Moreover, these ML models, such as Neural Networks or Random Forest, are not able to include pre-exisisting knowledge about a specific domain and are not designed to find causal relationships between variables. For these reasons, we wanted to investigate if Bayesian Networks were able to properly describe the hospital admission decision process. Bayesian Networks are probabilistic graphical models representing a set of variables and their conditional dependencies. The network structure was derived both from existing medical knowledge and from patients data collected during the first wave of the pandemic. While being explainable, we show that the Bayesian network has similar performance when compared to a less explainable ML model and that was able to generalize well across COVID-19 waves. © 2021 Copyright for this paper by its authors.