Neural Entity Recognition with Gazetteer based Fusion

• URL: http://arxiv.org/abs/2105.13225v1
• Date: Thu, 27 May 2021 15:14:15 GMT
• Title: Neural Entity Recognition with Gazetteer based Fusion
• Authors: Qing Sun, Parminder Bhatia
• Abstract summary: We propose an auxiliary gazetteer model and fuse it with an NER system, which results in better robustness and interpretability across different clinical datasets. Our gazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on novel entity mentions never presented during training.
• Score: 7.024494879945238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain. In this paper, we focus on clinical domain where only limited data is accessible and interpretability is important. Recent advancement in technology and the acceleration of clinical trials has resulted in the discovery of new drugs, procedures as well as medical conditions. These factors motivate towards building robust zero-shot NER systems which can quickly adapt to new medical terminology. We propose an auxiliary gazetteer model and fuse it with an NER system, which results in better robustness and interpretability across different clinical datasets. Our gazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on novel entity mentions never presented during training. Moreover, our fusion model is able to quickly adapt to new mentions in gazetteers without re-training and the gains from the proposed fusion model are transferable to related datasets.

Related papers

• GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models [1.123722364748134]
  This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) The methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon. The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98%.
  arXiv  Detail & Related papers  (2024-05-31T02:53:22Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• Extrinsic Factors Affecting the Accuracy of Biomedical NER [0.1529342790344802]
  Biomedical named entity recognition (NER) is a critial task that aims to identify structured information in clinical text. NER in the biomedical domain is challenging due to limited data availability.
  arXiv  Detail & Related papers  (2023-05-29T15:29:49Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• Deeper Clinical Document Understanding Using Relation Extraction [0.0]
  We propose a text mining framework comprising of Named Entity Recognition (NER) and Relation Extraction (RE) models. We introduce two new RE model architectures -- an accuracy-optimized one based on BioBERT and a speed-optimized one utilizing crafted features over a Fully Connected Neural Network (FCNN) We show two practical applications of this framework -- for building a biomedical knowledge graph and for improving the accuracy of mapping entities to clinical codes.
  arXiv  Detail & Related papers  (2021-12-25T17:14:13Z)
• Recognising Biomedical Names: Challenges and Solutions [9.51284672475743]
  We propose a transition-based NER model which can recognise discontinuous mentions. We also develop a cost-effective approach that nominates the suitable pre-training data. Our contributions have obvious practical implications, especially when new biomedical applications are needed.
  arXiv  Detail & Related papers  (2021-06-23T08:20:13Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)
• Med7: a transferable clinical natural language processing model for electronic health records [6.935142529928062]
  We introduce a named-entity recognition model for clinical natural language processing. The model is trained to recognise seven categories: drug names, route, frequency, dosage, strength, form, duration. We evaluate the transferability of the developed model using the data from the Intensive Care Unit in the US to secondary care mental health records (CRIS) in the UK.
  arXiv  Detail & Related papers  (2020-03-03T00:55:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.