60 citations · 70 across the 6 of their papers we have counts for
6 papers
Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims
Fan Ma, Yuntian Liu, Xiang Lan +22
Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative claims provide population-sca…
Advancing Pancreatic Cancer Prediction with a Next Visit Token Prediction Head on top of Med-BERT
Jianping He, Laila Rasmy, Degui Zhi +1
Background: Recently, numerous foundation models pretrained on extensive data have demonstrated efficacy in disease prediction using Electronic Health Records (EHRs). However, ther…
Prompting Large Language Models for Clinical Temporal Relation Extraction
Jianping He, Laila Rasmy, Haifang Li +5
Objective: This paper aims to prompt large language models (LLMs) for clinical temporal relation extraction (CTRE) in both few-shot and fully supervised settings. Materials and Met…
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
Shenghan Zhang, Haoxuan Li, Ruixiang Tang +5
Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) pro…
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data
Laila Rasmy, Jie Zhu, Zhiheng Li +7
Recently, there is great interest to investigate the application of deep learning models for the prediction of clinical events using electronic health records (EHR) data. In EHR da…
Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction
Laila Rasmy, Yang Xiang, Ziqian Xie +2
Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often re…