Search Results for author:
Found 50 papers, 26 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.
Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations
Current medical AI systems often fail to replicate real-world clinical reasoning, as they are predominantly trained and evaluated on static text and question-answer tasks.
Enhancing Patient-Centric Communication: Leveraging LLMs to Simulate Patient Perspectives
We evaluate and compare with human responses the comprehensibility of discharge summaries among individuals with varying educational backgrounds, using this analysis to assess the strengths and limitations of LLM-driven simulations.
Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?
On unseen i2b2 data, LLaMA-3-70B outperformed BERT by 7% (F1) on NER and 4% on RE.
Humans Continue to Outperform Large Language Models in Complex Clinical Decision-Making: A Study with Medical Calculators
Although large language models (LLMs) have been assessed for general medical knowledge using medical licensing exams, their ability to effectively support clinical decision-making tasks, such as selecting and using medical calculators, remains uncertain.
Demystifying Large Language Models for Medicine: A Primer
We then review the strategies, such as prompt engineering and fine-tuning, to adapt standard LLMs to specialized medical tasks.
MedINST: Meta Dataset of Biomedical Instructions
The integration of large language model (LLM) techniques in the field of medical analysis has brought about significant advancements, yet the scarcity of large, diverse, and well-annotated datasets remains a major challenge.
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment.
Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model
In external validations, LEME excelled in long-form QA with a Rouge-L of 0. 19 (all p<0. 0001), ranked second in MCQ accuracy (0. 68; all p<0. 0001), and scored highest in EHR summarization and clinical QA (ranging from 4. 24 to 4. 83 out of 5 for correctness, completeness, and readability).
Federated Graph Learning with Adaptive Importance-based Sampling
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required.
Towards Accountable AI-Assisted Eye Disease Diagnosis: Workflow Design, External Validation, and Continual Learning
Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability.
Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology
The evaluation focuses on factuality of evidence, selection and ranking of evidence, attribution of evidence, and answer accuracy and completeness.
Fair Text to Medical Image Diffusion Model with Subgroup Distribution Aligned Tuning
For evaluation, we set the target dataset to be enhanced as the BraST18 dataset, and trained a brain magnetic resonance (MR) slice-based gender classifier from it.
MedCalc-Bench: Evaluating Large Language Models for Medical Calculations
To this end, we propose MedCalc-Bench, a first-of-its-kind dataset focused on evaluating the medical calculation capability of LLMs.
Augmenting Biomedical Named Entity Recognition with General-domain Resources
Specifically, our models consistently outperformed the baseline models in six out of eight entity types, achieving an average improvement of 0. 9% over the best baseline performance across eight entities.
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
In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) along with ranking and re-ranking techniques, to improve the factuality of long-form question answering (QA) in the medical domain.
Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond
Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering.
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
This work underscores the importance of domain-specific data in developing medical LLMs and addresses the high computational costs involved in training, highlighting a balance between pre-training and fine-tuning strategies.
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.
A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations
The biomedical literature is rapidly expanding, posing a significant challenge for manual curation and knowledge discovery.
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)
