Tommaso Fontana

TL;DR: KG-COVID-19 is created, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs) and can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

TL;DR: Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction.

TL;DR: KG-COVID-19 is a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore CO VID-19 data and discover relationships.

TL;DR: In this article , a nonlinear similarity function is defined to map from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

TL;DR: In this paper , a 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative.