no code implementations • 13 Jul 2023 • Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini, Antonio Foncubierta-Rodriguez, Kristen L. Beck, Orest Boyko, Tanveer Syeda-Mahmood
With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data.
no code implementations • 25 Oct 2022 • Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini, Antonio Foncubierta-Rodriguez, Kristen L. Beck, Orest Boyko, Tanveer Syeda-Mahmood
All these nuances make simple methods of early, late, or intermediate fusion of features inadequate for outcome prediction.
1 code implementation • 25 Oct 2022 • Ben Schaper, Christopher Lohse, Marcell Streile, Andrea Giovannini, Richard Osuala
Despite extensive recent advances in summary generation models, evaluation of auto-generated summaries still widely relies on single-score systems insufficient for transparent assessment and in-depth qualitative analysis.
no code implementations • 28 Oct 2021 • Ivan Girardi, Panagiotis Vagenas, Dario Arcos-Díaz, Lydia Bessaï, Alexander Büsser, Ludovico Furlan, Raffaello Furlan, Mauro Gatti, Andrea Giovannini, Ellen Hoeven, Chiara Marchiori
We develop various AI models to predict hospitalization on a large (over 110$k$) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021.
1 code implementation • 31 Jul 2021 • Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou, Arjun Sharma, Joseph A. Paguio, Jasper S. Yao, Edward C. Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo A. Celi, Mehdi Moradi
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings.
no code implementations • 9 Jun 2021 • Kevin Thandiackal, Tiziano Portenier, Andrea Giovannini, Maria Gabrani, Orcun Goksel
In this work, we propose Genifer (GENeratIve FEature-driven image Replay), where a generative model is trained to replay images that must induce the same hidden features as real samples when they are passed through the classifier.
no code implementations • 9 Nov 2020 • Chiara Marchiori, Douglas Dykeman, Ivan Girardi, Adam Ivankay, Kevin Thandiackal, Mario Zusag, Andrea Giovannini, Daniel Karpati, Henri Saenz
Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider.