BioIE: Biomedical Information Extraction with Multi-head Attention
Enhanced Graph Convolutional Network
- URL: http://arxiv.org/abs/2110.13683v1
- Date: Tue, 26 Oct 2021 13:19:28 GMT
- Title: BioIE: Biomedical Information Extraction with Multi-head Attention
Enhanced Graph Convolutional Network
- Authors: Jialun Wu, Yang Liu, Zeyu Gao, Tieliang Gong, Chunbao Wang and Chen Li
- Abstract summary: 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.
- Score: 9.227487525657901
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Constructing large-scaled medical knowledge graphs can significantly boost
healthcare applications for medical surveillance, bring much attention from
recent research. An essential step in constructing large-scale MKG is
extracting information from medical reports. Recently, information extraction
techniques have been proposed and show promising performance in biomedical
information extraction. However, these methods only consider limited types of
entity and relation due to the noisy biomedical text data with complex entity
correlations. Thus, they fail to provide enough information for constructing
MKGs and restrict the downstream applications. To address this issue, we
propose Biomedical Information Extraction, a hybrid neural network to extract
relations from biomedical text and unstructured medical reports. Our model
utilizes a multi-head attention enhanced graph convolutional network to capture
the complex relations and context information while resisting the noise from
the data. 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. The results show that our
method achieves superior performance than baselines. Furthermore, we evaluate
the applicability of our method under a transfer learning setting and show that
BioIE achieves promising performance in processing medical text from different
formats and writing styles.
Related papers
- Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs [2.006175707670159]
PrimeKG++ is an enriched knowledge graph incorporating multimodal data.
Our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes.
arXiv Detail & Related papers (2025-01-03T05:29:12Z) - MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities [59.61465292965639]
This paper investigates a new paradigm for leveraging generative models in medical applications.
We propose a diffusion-based data engine, termed MRGen, which enables generation conditioned on text prompts and masks.
arXiv Detail & Related papers (2024-12-04T16:34:22Z) - Graph Relation Distillation for Efficient Biomedical Instance
Segmentation [80.51124447333493]
We propose a graph relation distillation approach for efficient biomedical instance segmentation.
We introduce two graph distillation schemes deployed at both the intra-image level and the inter-image level.
Experimental results on a number of biomedical datasets validate the effectiveness of our approach.
arXiv Detail & Related papers (2024-01-12T04:41:23Z) - Diversifying Knowledge Enhancement of Biomedical Language Models using
Adapter Modules and Knowledge Graphs [54.223394825528665]
We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models.
We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT.
We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
arXiv Detail & Related papers (2023-12-21T14:26:57Z) - High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models [1.9665865095034865]
We formulate the relation extraction task as binary classifications for large language models.
We designate the main title as the tail entity and explicitly incorporate it into the context.
Longer contents are sliced into text chunks, embedded, and retrieved with additional embedding models.
arXiv Detail & Related papers (2023-12-13T16:43:41Z) - ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical
Interactions Discovering [0.0]
We propose a novel Residual Message Graph Convolution Network (ResMGCN) for fast and precise biomedical interaction prediction.
We conduct experiments on four biomedical interaction network datasets, including protein-protein, drug-drug, drug-target, and gene-disease interactions.
arXiv Detail & Related papers (2023-11-13T13:16:35Z) - BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph
Construction and Analysis [0.4893345190925178]
We propose an end-to-end approach for knowledge extraction and analysis from biomedical clinical notes.
The proposed framework can successfully extract relevant structured information with high accuracy.
arXiv Detail & Related papers (2023-04-21T14:45:33Z) - EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations.
Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
arXiv Detail & Related papers (2022-08-01T18:47:03Z) - Scientific Language Models for Biomedical Knowledge Base Completion: An
Empirical Study [62.376800537374024]
We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction.
We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
arXiv Detail & Related papers (2021-06-17T17:55:33Z) - A Literature Review of Recent Graph Embedding Techniques for Biomedical
Data [36.446560017794845]
Many graph-based learning methods have been proposed to analyze such type of data.
The main difficulty is how to handle high dimensionality and sparsity of the biomedical graphs.
graph embedding methods provide an effective and efficient way to address the above issues.
arXiv Detail & Related papers (2021-01-17T01:53:50Z) - SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge
Graph Summarization [64.56399911605286]
We propose SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module.
SumGNN outperforms the best baseline by up to 5.54%, and the performance gain is particularly significant in low data relation types.
arXiv Detail & Related papers (2020-10-04T00:14:57Z)
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.