6 citations · 12 across the 6 of their papers we have counts for
6 papers
Before the Clinic: Transparent and Operable Design Principles for Healthcare AI
Alexander Bakumenko, Aaron J. Masino, Janine Hoelscher
The translation of artificial intelligence (AI) systems into clinical practice requires bridging fundamental gaps between explainable AI theory, clinician expectations, and governa…
PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction
Ranga Baminiwatte, Kazi Jewel Rana, Aaron J. Masino
Understanding disease similarity is critical for advancing diagnostics, drug discovery, and personalized treatment strategies. We present PhenoGnet, a novel graph-based contrastive…
Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond
Jacob R. Epifano, Stephen Glass, Ravi P. Ramachandran +3
This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and bey…
Revisiting the Fragility of Influence Functions
Jacob R. Epifano, Ravi P. Ramachandran, Aaron J. Masino +1
In the last few years, many works have tried to explain the predictions of deep learning models. Few methods, however, have been proposed to verify the accuracy or faithfulness of…
Opening Access to Visual Exploration of Audiovisual Digital Biomarkers: an OpenDBM Analytics Tool
Carla Floricel, Jacob Epifano, Stephanie Caamano +4
Digital biomarkers (DBMs) are a growing field and increasingly tested in the therapeutic areas of psychiatric and neurodegenerative disorders. Meanwhile, isolated silos of knowledg…
Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypes
Aaron J. Masino, Kaitlin A. Folweiler
Baseline injury categorization is important to traumatic brain injury (TBI) research and treatment. Current categorization is dominated by symptom-based scores that insufficiently…