Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework

• URL: http://arxiv.org/abs/2009.09509v1
• Date: Sun, 20 Sep 2020 19:50:28 GMT
• Title: Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework
• Authors: Shweta Yadav, Srivatsa Ramesh, Sriparna Saha, and Asif Ekbal
• Abstract summary: We study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. We introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text.
• Score: 41.4339656250053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To minimize the accelerating amount of time invested in the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in multi-task learning (MTL) framework and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves overall the baselines (deep learning techniques) and single-task models for predicting the relationships, without compromising on the performance of all the tasks.

Related papers

• EMBRE: Entity-aware Masking for Biomedical Relation Extraction [12.821610050561256]
  We introduce the Entity-aware Masking for Biomedical Relation Extraction (EMBRE) method for relation extraction. Specifically, we integrate entity knowledge into a deep neural network by pretraining the backbone model with an entity masking objective.
  arXiv  Detail & Related papers  (2024-01-15T18:12:01Z)
• ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction [3.263873198567265]
  ReOnto Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a vocabulary. We present a novel technique that makes use of neuro symbolic knowledge for the RE task. Experimental results on two public biomedical datasets, BioRel and ADE, show that our method outperforms all the baselines.
  arXiv  Detail & Related papers  (2023-09-04T05:36:58Z)
• Similarity-based Memory Enhanced Joint Entity and Relation Extraction [3.9659135716762894]
  Document-level joint entity and relation extraction is a challenging information extraction problem. We present a multi-task learning framework with bidirectional memory-like dependency between tasks. Our empirical studies show that the proposed approach outperforms the existing methods.
  arXiv  Detail & Related papers  (2023-07-14T12:26:56Z)
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design [64.69434941796904]
  We propose a novel setting and models for in-context drug synergy learning. We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context.
  arXiv  Detail & Related papers  (2023-06-19T17:03:46Z)
• BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network [9.227487525657901]
  We propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports. We evaluate our model on two major biomedical relationship extraction tasks, chemical-disease relation and chemical-protein interaction, and a cross-hospital pan-cancer pathology report corpus.
  arXiv  Detail & Related papers  (2021-10-26T13:19:28Z)
• Discovering Drug-Target Interaction Knowledge from Biomedical Literature [107.98712673387031]
  The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from literature becomes an urgent demand in the industry. We explore the first end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
  arXiv  Detail & Related papers  (2021-09-27T17:00:14Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
  We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-11-03T11:00:10Z)
• Uncovering the Corona Virus Map Using Deep Entities and Relationship Models [1.6263770627425762]
  We extract entities and relationships related to COVID-19 from a corpus of articles related to Corona virus. We employ a concept masking paradigm to prevent the evolution of neural networks functioning as an associative memory.
  arXiv  Detail & Related papers  (2020-09-07T12:48:36Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

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.