Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension

• URL: http://arxiv.org/abs/2303.08262v1
• Date: Tue, 14 Mar 2023 22:37:31 GMT
• Title: Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
• Authors: Cheng Peng, Xi Yang, Zehao Yu, Jiang Bian, William R. Hogan, Yonghui Wu
• Abstract summary: We formulate both clinical concept extraction and relation extraction using a unified prompt-based machine reading comprehension architecture. We compare our MRC models with existing deep learning models for concept extraction and end-to-end relation extraction. We evaluate the transfer learning ability of the proposed MRC models in a cross-institution setting.
• Score: 38.79665143111312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications. Methods: We formulate both clinical concept extraction and relation extraction using a unified prompt-based MRC architecture and explore state-of-the-art transformer models. We compare our MRC models with existing deep learning models for concept extraction and end-to-end relation extraction using two benchmark datasets developed by the 2018 National NLP Clinical Challenges (n2c2) challenge (medications and adverse drug events) and the 2022 n2c2 challenge (relations of social determinants of health [SDoH]). We also evaluate the transfer learning ability of the proposed MRC models in a cross-institution setting. We perform error analyses and examine how different prompting strategies affect the performance of MRC models. Results and Conclusion: The proposed MRC models achieve state-of-the-art performance for clinical concept and relation extraction on the two benchmark datasets, outperforming previous non-MRC transformer models. GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the two datasets by 1%~3% and 0.7%~1.3%, respectively. For end-to-end relation extraction, GatorTron-MRC and BERT-MIMIC-MRC achieve the best F1-scores, outperforming previous deep learning models by 0.9%~2.4% and 10%-11%, respectively. For cross-institution evaluation, GatorTron-MRC outperforms traditional GatorTron by 6.4% and 16% for the two datasets, respectively. The proposed method is better at handling nested/overlapped concepts, extracting relations, and has good portability for cross-institute applications.

Related papers

• Multi-objective Representation for Numbers in Clinical Narratives Using CamemBERT-bio [0.9208007322096533]
  This research aims to classify numerical values extracted from medical documents across seven physiological categories. We introduce two main innovations: integrating keyword embeddings into the model and adopting a number-agnostic strategy. We show substantial improvements in the effectiveness of CamemBERT-bio, surpassing conventional methods with an F1 score of 0.89.
  arXiv  Detail & Related papers  (2024-05-28T01:15:21Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• An Empirical Study of Multimodal Model Merging [148.48412442848795]
  Model merging is a technique that fuses multiple models trained on different tasks to generate a multi-task solution. We conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture. We propose two metrics that assess the distance between weights to be merged and can serve as an indicator of the merging outcomes.
  arXiv  Detail & Related papers  (2023-04-28T15:43:21Z)
• UNIMIB at TREC 2021 Clinical Trials Track [2.840363325289377]
  This contribution summarizes the participation of the UNIMIB team to the TREC 2021 Clinical Trials Track. We have investigated the effect of different query representations combined with several retrieval models on the retrieval performance.
  arXiv  Detail & Related papers  (2022-07-27T13:39:30Z)
• Learning brain MRI quality control: a multi-factorial generalization problem [0.0]
  This work aimed at evaluating the performances of the MRIQC pipeline on various large-scale datasets. We focused our analysis on the MRIQC preprocessing steps and tested the pipeline with and without them. We concluded that a model trained with data from a heterogeneous population, such as the CATI dataset, provides the best scores on unseen data.
  arXiv  Detail & Related papers  (2022-05-31T15:46:44Z)
• RoBLEURT Submission for the WMT2021 Metrics Task [72.26898579202076]
  We present our submission to the Shared Metrics Task: RoBLEURT. Our model reaches state-of-the-art correlations with the WMT 2020 human annotations upon 8 out of 10 to-English language pairs.
  arXiv  Detail & Related papers  (2022-04-28T08:49:40Z)
• A New Entity Extraction Method Based on Machine Reading Comprehension [4.92025078254413]
  This paper presents an effective MRC-based entity extraction model-MRC-I2DP. It uses the proposed gated attention-attracting mechanism to adjust the restoration of each part of the text pair. It also uses the proposed 2D probability coding module, TALU function and mask mechanism to strengthen the detection of all possible targets of the target.
  arXiv  Detail & Related papers  (2021-08-14T02:11:41Z)
• Clinical Relation Extraction Using Transformer-based Models [28.237302721228435]
  We developed a series of clinical RE models based on three transformer architectures, namely BERT, RoBERTa, and XLNet. We demonstrated that the RoBERTa-clinical RE model achieved the best performance on the 2018 MADE1.0 dataset with an F1-score of 0.8958. Our results indicated that the binary classification strategy consistently outperformed the multi-class classification strategy for clinical relation extraction.
  arXiv  Detail & Related papers  (2021-07-19T15:15:51Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

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.