ClinicalRadioBERT: Knowledge-Infused Few Shot Learning for Clinical Notes Named Entity Recognition

  • Saed Rezayi
  • , Haixing Dai
  • , Zhengliang Liu
  • , Zihao Wu
  • , Akarsh Hebbar
  • , Andrew H. Burns
  • , Lin Zhao
  • , Dajiang Zhu
  • , Quanzheng Li
  • , Wei Liu
  • , Sheng Li
  • , Tianming Liu
  • , Xiang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Transformer based language models such as BERT have been widely applied to many domains through model pretraining and fine tuning. However, in low-resource scenarios such as clinical cases, customizing a BERT-based language model is still a challenging task. In this paper, we focus on the radiotherapy domain and train a ClinicalRadioBERT model for analyzing clinical notes through a two-step procedure. First, we fine tune a BioBERT model by exploiting full texts of radiotherapy literature and name this model as RadioBERT. Second, we propose a knowledge-infused few-shot learning (KI-FSL) approach that leverages domain knowledge and trains the ClinicalRadioBERT model for understanding radiotherapy clinical notes. We evaluate ClinicalRadioBERT on a newly collected clinical notes dataset and demonstrate its superiority over baselines on few-shot named entity recognition. We will apply the ClinicalRadioBERT to link BERT and medical imaging for radiotherapy.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsChunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Zhiming Cui
PublisherSpringer Science and Business Media Deutschland GmbH
Pages269-278
Number of pages10
ISBN (Print)9783031210136
DOIs
StatePublished - 2022
Externally publishedYes
Event13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 18 2022Sep 18 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13583 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period9/18/229/18/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Biomedical language models
  • Named entity recognition
  • Natural language processing

Fingerprint

Dive into the research topics of 'ClinicalRadioBERT: Knowledge-Infused Few Shot Learning for Clinical Notes Named Entity Recognition'. Together they form a unique fingerprint.

Cite this