MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction

• URL: http://arxiv.org/abs/2204.04779v1
• Date: Sun, 10 Apr 2022 22:07:25 GMT
• Title: MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction
• Authors: Saadullah Amin, Pasquale Minervini, David Chang, G\"unter Neumann, Pontus Stenetorp
• Abstract summary: Distant supervision is commonly used to tackle the scarcity of annotated data. Bio-DSRE models can seemingly produce very accurate results in several benchmarks. However, given the challenging nature of the task, we set out to investigate the validity of such impressive results.
• Score: 19.046156065686308
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Relation Extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts. Distant supervision is commonly used as a way to tackle the scarcity of annotated data by automatically pairing knowledge graph relationships with raw texts. Distantly Supervised Biomedical Relation Extraction (Bio-DSRE) models can seemingly produce very accurate results in several benchmarks. However, given the challenging nature of the task, we set out to investigate the validity of such impressive results. We probed the datasets used by Amin et al. (2020) and Hogan et al. (2021) and found a significant overlap between training and evaluation relationships that, once resolved, reduced the accuracy of the models by up to 71%. Furthermore, we noticed several inconsistencies with the data construction process, such as creating negative samples and improper handling of redundant relationships. We mitigate these issues and present MedDistant19, a new benchmark dataset obtained by aligning the MEDLINE abstracts with the widely used SNOMED Clinical Terms (SNOMED CT) knowledge base. We experimented with several state-of-the-art models achieving an AUC of 55.4% and 49.8% at sentence- and bag-level, showing that there is still plenty of room for improvement.

Related papers

• Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks [0.7071166713283337]
  We created datasets large enough to train machine learning models. Our goal is to label behaviors corresponding to autism criteria. Augmenting data increased recall by 13% but decreased precision by 16%.
  arXiv  Detail & Related papers  (2024-05-08T03:18:12Z)
• Detecting and clustering swallow events in esophageal long-term high-resolution manometry [48.688209040613216]
  We propose a Deep Learning based swallowing detection method to accurately identify swallowing events and secondary non-deglutitive-induced esophageal motility disorders. We evaluate our computational pipeline on a total of 25 LTHRMs, which were meticulously annotated by medical experts.
  arXiv  Detail & Related papers  (2024-05-02T09:41:31Z)
• Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions [80.88681952022479]
  We introduce a new method for combining semi-supervised with multiple-instance learning. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains in per-subject tremor detection.
  arXiv  Detail & Related papers  (2023-04-29T12:25:10Z)
• Does Synthetic Data Generation of LLMs Help Clinical Text Mining? [51.205078179427645]
  We investigate the potential of OpenAI's ChatGPT to aid in clinical text mining. We propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data. Our method has resulted in significant improvements in the performance of downstream tasks.
  arXiv  Detail & Related papers  (2023-03-08T03:56:31Z)
• Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs [61.080598804629375]
  People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
  arXiv  Detail & Related papers  (2022-12-20T12:56:28Z)
• Adding more data does not always help: A study in medical conversation summarization with PEGASUS [5.276054618115727]
  We study the effect of dataset size on transfer learning medical conversation summarization using PEG. We also evaluate various iterative labeling strategies in the low-data regime, following their success in the classification setting. Our work sheds light on the successes and challenges of translating low-data regime techniques in classification to medical conversation summarization.
  arXiv  Detail & Related papers  (2021-11-15T07:27:35Z)
• 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)
• On the diminishing return of labeling clinical reports [2.1431637042179683]
  We show that performant medical NLP models may be obtained with small amount of labeled data. We show quantitatively the effect of training data size on a fixed test set composed of two of the largest public chest x-ray radiology report datasets.
  arXiv  Detail & Related papers  (2020-10-27T19:51:04Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification [2.985964157078619]
  Automatic analysis of diseases in the GI tract is a hot topic in computer science and medical-related journals. A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level. We present comprehensive evaluations of five distinct machine learning models that can classify 16 different GI tract conditions.
  arXiv  Detail & Related papers  (2020-05-08T08:59:31Z)

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.