91 citations · 145 across the 9 of their papers we have counts for
5 papers · 1 filter
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?
Byung-Hak Kim, Zhongfen Deng, Philip S. Yu +1
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) c…
Deep Claim: Payer Response Prediction from Claims Data with Deep Learning
Byung-Hak Kim, Seshadri Sridharan, Andy Atwal +1
Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood…
LumièreNet: Lecture Video Synthesis from Audio
Byung-Hak Kim, Varun Ganapathi
We present LumièreNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new au…
GritNet: Student Performance Prediction with Deep Learning
Byung-Hak Kim, Ethan Vizitei, Varun Ganapathi
Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-sta…
Constrained Approximate Maximum Entropy Learning of Markov Random Fields
Varun Ganapathi, David Vickrey, John Duchi +1
Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient descent procedure. Replacing exa…