Search Results for author:
Found 36 papers, 19 papers with code
Automatic recognition of abdominal lymph nodes from clinical text
We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.
Whose Side Are You On? Investigating the Political Stance of Large Language Models
Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval.
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities.
Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond
Uncertainty estimation plays a pivotal role in ensuring the reliability of safety-critical human-AI interaction systems, particularly in the medical domain.
AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning
Clinical calculators play a vital role in healthcare by offering accurate evidence-based predictions for various purposes such as prognosis.
Me LLaMA: Foundation Large Language Models for Medical Applications
In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.
PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge
PubTator 3. 0 (https://www. ncbi. nlm. nih. gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals.
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
Our research demonstrates the effectiveness of using UMLS-augmented LLMs and highlights the potential application value of LLMs in in medical question-answering.
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval
In response, we introduce MedCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot semantic IR in biomedicine.
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation.
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health.
Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations
Biomedical literature is growing rapidly, making it challenging to curate and extract knowledge manually.
GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions.
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images.
Bioformer: an efficient transformer language model for biomedical text mining
We thoroughly evaluated the performance of Bioformer as well as existing biomedical BERT models including BioBERT and PubMedBERT on 15 benchmark datasets of four different biomedical NLP tasks: named entity recognition, relation extraction, question answering and document classification.
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering.
LitCovid in 2022: an information resource for the COVID-19 literature
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 COVID-19.
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
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 SARS-CoV-2 infection.
Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
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 mention is normalized and linked to a database record or identifier by a text-mining algorithm.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation
However, it has been a primary curation bottleneck due to the nature of the task and the rapid literature growth.
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis
To evaluate our proposed method's utility and privacy loss, we apply our model on a medical report disease label classification task using two noisy challenging clinical text datasets.
Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration
The objective was to develop and evaluate the performance of a novel 'M3' deep learning framework on RPD detection.
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
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 spread.
Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view
In this study we analyze the LitCovid collection, 13, 369 COVID-19 related articles found in PubMed as of May 15th, 2020 with the purpose of examining the landscape of literature and presenting it in a format that facilitates information navigation and understanding.
Predicting risk of late age-related macular degeneration using deep learning
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide.
An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
Multi-task learning (MTL) has achieved remarkable success in natural language processing applications.
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
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-protein interaction detection, gene-drug association prediction, and biomedical literature-based discovery.
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~13%.
A deep learning approach for automated detection of geographic atrophy from color fundus photographs
Results: The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUC of 0. 933-0. 976, 0. 939-0. 976, and 0. 827-0. 888, respectively.
A multi-task deep learning model for the classification of Age-related Macular Degeneration
Built on our previous work DeepSeeNet, we developed a novel deep learning model for automated classification of images into the 9-step scale.
Age-Related Macular Degeneration Classification
Classification
+2
DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs
DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale.
ML-Net: multi-label classification of biomedical texts with deep neural networks
Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.
BioSentVec: creating sentence embeddings for biomedical texts
Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods.
Ranked #1 on
Sentence Embeddings For Biomedical Texts
on MedSTS
(using extra training data)
