Search Results for author: Jason Alan Fries

Found 6 papers, 2 papers with code

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

no code implementations23 Nov 2021 Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren

Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.

Computed Tomography (CT) Fairness +1

The accuracy vs. coverage trade-off in patient-facing diagnosis models

no code implementations11 Dec 2019 Anitha Kannan, Jason Alan Fries, Eric Kramer, Jen Jen Chen, Nigam Shah, Xavier Amatriain

A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process.

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018

no code implementations17 Nov 2018 Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi

This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.

Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction

no code implementations4 Jun 2016 Jason Alan Fries

We submitted two systems to the SemEval-2016 Task 12: Clinical TempEval challenge, participating in Phase 1, where we identified text spans of time and event expressions in clinical notes and Phase 2, where we predicted a relation between an event and its parent document creation time.

Entity Extraction using GAN General Classification +3

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