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Found 15 papers, 3 papers with code
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
This study underscores the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice.
Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes
Data accuracy is essential for scientific research and policy development.
AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events
Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs).
Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships.
Distributionally Robust Cross Subject EEG Decoding
In this work, we propose a principled approach to perform dynamic evolution on the data for improvement of decoding robustness.
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine.
Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research
Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc.
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
The 1, 672, 110 filtered triples were used to train with knowledge graph completion algorithms (i. e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.
Knowledge Graph-based Neurodegenerative Diseases and Diet Relationship Discovery
To date, there are no effective treatments for most neurodegenerative diseases.
Semantic based model of Conceptual Work Products for formal verification of complex interactive systems
As a first step, we illustrate how graphical class and state diagrams from UML can be developed and critiqued with subject matter experts to serve as specifications of the conceptual work product of case management.
Towards an Ontology-based Medication Conversational Agent for PrEP and PEP
ABSTRACT: HIV (human immunodeficiency virus) can damage a human{'}s immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death.
Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction
Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks.
Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States
We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).
Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning
In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input.
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.
