91 citations · 164 across the 15 of their papers we have counts for
6 papers · 1 filter
Medical Codes Prediction from Clinical Notes: From Human Coders to Machines
Byung-Hak Kim
Prediction of medical codes from clinical notes is a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation w…
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…
Deep Learning to Predict Student Outcomes
Byung-Hak Kim
The increasingly fast development cycle for online course contents, along with the diverse student demographics in each online classroom, make real-time student outcomes prediction…
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…