Hua Cheng — accepted AI-conference papers

2023

MDACE: MIMIC Documents Annotated with Code Evidence

Hua Cheng, Rana Jafari, April Russell, Russell Klopfer, Edmond Lu, Benjamin Striner +1

ACL 2023long

We introduce a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. One such task is Computer-Assisted Coding (CAC) which has improved significantly in recent years, thanks to advances in machine learning technologies. Yet simply predic…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗

2021

Effective Convolutional Attention Network for Multi-label Clinical Document Classification

Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, Thomas Schaaf

EMNLP 2021main

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. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction f…

Cited by 65 Source ↗PDF ↗Scholar ↗