Repository for Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset (AMIA 2020 INFORMATICS SUMMIT)
Recommendations are extracted with Hierarchical Attention Networks model. Entities are extracted with NeuroNER.
Clinical experimental data are not included to comply with HIPAA Privacy.
Wilson Lau, Thomas H. Payne, MD, Ozlem Uzuner, PhD, Meliha Yetisgen, PhD
@inproceedings{ wilson2020amiasummit,
title = "Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset",
author = "Wilson Lau, Thomas H. Payne, MD, Ozlem Uzuner, PhD, Meliha Yetisgen, PhD",
booktitle = "Proceedings of the AMIA 2020 INFORMATICS SUMMIT",
month = march,
year = "2020",
address = "Houston, Texas, USA",
publisher = "American Medical Informatics Association",
url = "https://knowledge.amia.org/71623-amia-1.4589302/t0003-1.4591021/t0003-1.4591022/a048-1.4591158/an048-1.4591159" or "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233090/"
}