133 citations · 825 across the 36 of their papers we have counts for
39 papers
Learning to Ask Like a Physician
Eric Lehman, Vladislav Lialin, Katelyn Y. Legaspi +15
Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information n…
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
Di Jin, Elena Sergeeva, Wei-Hung Weng +2
The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research…
Structure Inducing Pre-Training
Matthew B. A. McDermott, Brendan Yap, Peter Szolovits +1
Language model pre-training and derived methods are incredibly impactful in machine learning. However, there remains considerable uncertainty on exactly why pre-training helps impr…
Adversarial Contrastive Pre-training for Protein Sequences
Matthew B. A. McDermott, Brendan Yap, Harry Hsu +2
Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks. These ideas ha…
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Di Jin, Eileen Pan, Nassim Oufattole +3
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present t…
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Geeticka Chauhan, Ruizhi Liao, William Wells +6
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiog…