393 citations · 684 across the 70 of their papers we have counts for
8 papers · 1 filter
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Chengsheng Mao, Jie Xu, Luke Rasmussen +9
Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from…
Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction
Shaika Chowdhury, Chenwei Zhang, Philip S. Yu +1
Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients. Electronic health notes serve as a…
Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms
Yizhen Zhong, Luke Rasmussen, Yu Deng +10
The automatic development of phenotype algorithms from Electronic Health Record data with machine learning (ML) techniques is of great interest given the current practice is very t…
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
Yuan Luo, Peter Szolovits
This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house develo…
Graph Convolutional Networks for Text Classification
Liang Yao, Chengsheng Mao, Yuan Luo
Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolutio…
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
Liang Yao, Chengsheng Mao, Yuan Luo
Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based featur…