Papers Medical Relation Extraction

Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension

Extracting causal relationships from a medical case report is essential for comprehending the case, particularly its diagnostic process. Since the diagnostic process is regarded as a bottom-up inference, causal relations…

Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution

Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data. In order to handle the heterogeneous label distribution across clients, most …

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., depende…

LinkBERT: Pretraining Language Models with Document Links

Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that s…

GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the…

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applicat…

FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction

Unlike other domains, medical texts are inevitably accompanied by private information, so sharing or copying these texts is strictly restricted. However, training a medical relation extraction model requires collecting t…

A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction

Joint medical relation extraction refers to extracting triples, composed of entities and relations, from the medical text with a single model. One of the solutions is to convert this task into a sequential tagging task. …

Leveraging Dependency Forest for Neural Medical Relation Extraction

Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain,…

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical liter…

A hybrid deep learning approach for medical relation extraction

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. W…

Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network

Simultaneous administration of multiple drugs can have synergistic or antagonistic effects as one drug can affect activities of other drugs. Synergistic effects lead to improved therapeutic outcomes, whereas, antagonisti…

Crowdsourcing Ground Truth for Medical Relation Extraction

Cognitive computing systems require human labeled data for evaluation, and often for training. The standard practice used in gathering this data minimizes disagreement between annotators, and we have found this results i…

Medical Relation Extraction with Manifold Models