368 citations · 1.8k across the 44 of their papers we have counts for
24 papers · 1 filter
Matching Patients to Clinical Trials with Large Language Models
Qiao Jin, Zifeng Wang, Charalampos S. Floudas +7
Patient recruitment is challenging for clinical trials. We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models. TrialGPT…
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
Ling Luo, Chih-Hsuan Wei, Po-Ting Lai +3
Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mi…
Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
Ling Luo, Chih-Hsuan Wei, Po-Ting Lai +3
The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene menti…
LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation
Qingyu Chen, Jingcheng Du, Alexis Allot +1
The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a lite…
tmVar 3.0: an improved variant concept recognition and normalization tool
Chih-Hsuan Wei, Alexis Allot, Kevin Riehle +2
Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large sc…
BioRED: A Rich Biomedical Relation Extraction Dataset
Ling Luo, Po-Ting Lai, Chih-Hsuan Wei +2
Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most exist…
Radiology Text Analysis System (RadText): Architecture and Evaluation
Song Wang, Mingquan Lin, Ying Ding +3
Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads o…
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis
Qiyuan An, Ruijiang Li, Lin Gu +5
Unsupervised domain adaptation (UDA) generally aligns the unlabeled target domain data to the distribution of the source domain to mitigate the distribution shift problem. The stan…
BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer
Po-Ting Lai, Zhiyong Lu
A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation. To automatically extract…
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
Qingyu Chen, Robert Leaman, Alexis Allot +4
The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its…
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology
Ling Luo, Shankai Yan, Po-Ting Lai +7
Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dic…
An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
Yifan Peng, Qingyu Chen, Zhiyong Lu
Multi-task learning (MTL) has achieved remarkable success in natural language processing applications. In this work, we study a multi-task learning model with multiple decoders on…
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
Qingyu Chen, Kyubum Lee, Shankai Yan +3
Capturing the semantics of related biological concepts, such as genes and mutations, is of significant importance to many research tasks in computational biology such as protein-pr…
Biomedical Mention Disambiguation using a Deep Learning Approach
Chih-Hsuan Wei, Kyubum Lee, Robert Leaman +1
Automatically locating named entities in natural language text - named entity recognition - is an important task in the biomedical domain. Many named entity mentions are ambiguous…
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
Qingyu Chen, Jingcheng Du, Sun Kim +2
Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general…
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
Yifan Peng, Shankai Yan, Zhiyong Lu
Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate res…
A self-attention based deep learning method for lesion attribute detection from CT reports
Yifan Peng, Ke Yan, Veit Sandfort +2
In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.…
Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction
Yijia Zhang, Zhiyong Lu
The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-protein interactions (CPIs). B…
BioSentVec: creating sentence embeddings for biomedical texts
Qingyu Chen, Yifan Peng, Zhiyong Lu
Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods. Although pre-trained sen…
Personalized neural language models for real-world query auto completion
Nicolas Fiorini, Zhiyong Lu
Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user typ…
Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models
Yifan Peng, Anthony Rios, Ramakanth Kavuluru +1
Text mining the relations between chemicals and proteins is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of s…
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Yifan Peng, Xiaosong Wang, Le Lu +3
Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction. Here, we p…
Deep learning for extracting protein-protein interactions from biomedical literature
Yifan Peng, Zhiyong Lu
State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to in…
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents
Sun Kim, Nicolas Fiorini, W. John Wilbur +1
The main approach of traditional information retrieval (IR) is to examine how many words from a query appear in a document. A drawback of this approach, however, is that it may fai…