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20152023
most citedGenerating Multi-label Discrete Patient Records using Generative Adversarial Networks

360 citations · 2k across the 47 of their papers we have counts for

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11 papers · 1 filter

cs.CL2023★ 5 cited

Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes

Sunjun Kweon, Junu Kim, Jiyoun Kim +12

The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict pr…

cs.CL2023

Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports

Hyunseung Chung, Jiho Kim, Joon-myoung Kwon +3

Electrocardiogram (ECG) synthesis is the area of research focused on generating realistic synthetic ECG signals for medical use without concerns over annotation costs or clinical d…

cs.CL2023★ 13 cited

EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records

Gyubok Lee, Hyeonji Hwang, Seongsu Bae +6

We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insuranc…

cs.CL2022★ 1 cited

Specializing Multi-domain NMT via Penalizing Low Mutual Information

Jiyoung Lee, Hantae Kim, Hyunchang Cho +2

Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. It is appealing because of its efficacy in handling multiple domains within one model. An…

cs.CL2022

Do Language Models Understand Measurements?

Sungjin Park, Seungwoo Ryu, Edward Choi

Recent success of pre-trained language models (PLMs) has stimulated interest in their ability to understand and work with numbers. Yet, the numerical reasoning over measurements ha…

cs.CL2022★ 1 cited

Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records

Daeyoung Kim, Seongsu Bae, Seungho Kim +1

Question Answering on Electronic Health Records (EHR-QA) has a significant impact on the healthcare domain, and it is being actively studied. Previous research on structured EHR-QA…