Search Results for author: Hua Cheng

Found 5 papers, 2 papers with code

Effective Convolutional Attention Network for Multi-label Clinical Document Classification

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.

Classification Document Classification +1

Understanding data analysis aspects of TMS-EEG in clinical study: a mini review and a case study with open dataset

no code implementations9 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.

EEG

MDACE: MIMIC Documents Annotated with Code Evidence

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.

Document Classification Extreme Multi-Label Classification

Spinopelvic Anatomic Parameters Prediction Model of NSLBP based on data mining

no code implementations14 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.

regression

Posterior Calibrated Training on Sentence Classification Tasks

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.

Classification General Classification +2

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