Majid Afshar

Latest

LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning
Current and future state of evaluation of large language models for medical summarization tasks
Detecting Stigmatizing Language in Clinical Notes with Large Language Models for Addiction Care
Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies
On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models
Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability
Prompt Engineering GPT-4 to Answer Patient Inquiries: A Real-Time Implementation in the Electronic Health Record across Provider Clinics
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
DR. BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models