AI-assisted literature screening: A hybrid approach using large language models and retrieval-augmented generation
This study aims to improve literature screening efficiency and accuracy by developing an LLM-based approach that incorporates rule-based preprocessing, prompt engineering (including RAG), and ensemble techniques.
Nov 30, 2025
Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review
We conduct a comprehensive review of recent studies that leverage generative LLMs for EHR analysis and applications, focusing on their performance and strategies for improvement.
Aug 25, 2025
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning
We propose an asymmetric contrastive multimodal learning framework, an effective and training-efficient framework tailored for molecules, promoting cross-modality understanding between the molecular graph and other chemical modalities.
Jul 23, 2025
Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases
This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.
Jun 11, 2024
A machine learning approach to robustly determine director fields and analyze defects in active nematics
We developed a machine learning model for extracting reliable director fields from raw experimental images, which enables accurate analysis of topological defects.
Jan 31, 2024
Motif-based graph representation learning with application to chemical molecules
We designed a novel convolution module for graph representational learning on molecules with an efficient pretraining strategy, enabling the capture of local structural and semantic information from graph motifs.
Jan 11, 2023
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph
We developed an algebraic framework for learning consistent relation embeddings in knowledge graphs and proposed an algebraic-based instantiation for a knowledge graph embedding model.
May 3, 2022