no code implementations • EMNLP 2021 • Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, Thomas Schaaf
Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels.
Ranked #2 on Medical Code Prediction on MIMIC-III
no code implementations • 9 Mar 2024 • Hua Cheng
Concurrency of transcranial magnetic stimulation with electroencephalography (TMS-EEG) technique is a powerful and challenging methodology for basic research and clinical applications.
1 code implementation • ACL 2023 • Hua Cheng, Rana Jafari, April Russell, Russell Klopfer, Edmond Lu, Benjamin Striner, Matthew R. Gormley
In this paper, we introduce MDACE, the first publicly available code evidence dataset, which is built on a subset of the MIMIC-III clinical records.
no code implementations • 14 Sep 2020 • Hua Cheng
Objective: The purpose of this study is to perform analysis through the low back pain open data set to predict the incidence of non-specific chronic low back pain (NSLBP) to obtain a more accurate and convenient sagittal spinopelvic parameter model.
1 code implementation • ACL 2020 • Taehee Jung, Dongyeop Kang, Hua Cheng, Lucas Mentch, Thomas Schaaf
Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities. We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives.