PULSAR: Pre-training with Extracted Healthcare Terms for Summarising
Patients' Problems and Data Augmentation with Black-box Large Language Models

PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models

5 June 2023

Viktor Schlegel

R. Batista-Navarro

Thanh-Tung Nguyen

Abhinav Ramesh Kashyap

Papers citing "PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models"

5 / 5 papers shown

Title
TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation Bowen Deng Chang Xu H. Li Yuhao Huang Min Hou Jiang Bian MedIm 35 0 0 24 Apr 2025
Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks Chancellor R. Woolsey Prakash Bisht Joshua M Rothman Gondy Leroy AI4MH LM&MA SyDa 20 0 0 08 May 2024
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations Longxiang Zhang Renato M. P. Negrinho Arindam Ghosh V. Jagannathan H. Hassanzadeh Thomas Schaaf Matthew R. Gormley LM&MA AI4MH 68 67 0 24 Sep 2021
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization Griffin Adams Emily Alsentzer Mert Ketenci Jason Zucker Noémie Elhadad 33 46 0 12 Apr 2021
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP Timo Schick Sahana Udupa Hinrich Schütze 257 374 0 28 Feb 2021