368 citations · 1.8k across the 44 of their papers we have counts for
45 papers
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…
LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search
Qiao Jin, Ashley Shin, Zhiyong Lu
Queries with similar information needs tend to have similar document clicks, especially in biomedical literature search engines where queries are generally short and top documents…
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…
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
Robert Leaman, Rezarta Islamaj, Alexis Allot +3
A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of S…
LitCovid in 2022: an information resource for the COVID-19 literature
Qingyu Chen, Alexis Allot, Robert Leaman +5
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on…
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…
Lymph Node Detection in T2 MRI with Transformers
Tejas Sudharshan Mathai, Sungwon Lee, Daniel C. Elton +4
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases. The…
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…
Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration
Qingyu Chen, Tiarnan D. L. Keenan, Alexis Allot +14
Objective Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and…
A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2
Robert Leaman, Zhiyong Lu
The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-establ…
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…
Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view
Lana Yeganova, Rezarta Islamaj, Qingyu Chen +8
Timely access to accurate scientific literature in the battle with the ongoing COVID-19 pandemic is critical. This unprecedented public health risk has motivated research towards u…
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…
Predicting risk of late age-related macular degeneration using deep learning
Yifan Peng, Tiarnan D. Keenan, Qingyu Chen +5
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-t…
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…
TeamTat: a collaborative text annotation tool
Rezarta Islamaj, Dongseop Kwon, Sun Kim +1
Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given th…
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…
DTF: Deep Tensor Factorization for Predicting Anticancer Drug Synergy
Zexuan Sun, Shujun Huang, Peiran Jiang +1
Motivation: Combination therapies have been widely used to treat cancers. However, it is cost- and time-consuming to experimentally screen synergistic drug pairs due to the enormou…
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…