146 citations
- Northwestern UniversityUS20 papers
- Northern Illinois UniversityUS5 papers
- Argonne National LaboratoryUS3 papers
- California Institute of TechnologyUS3 papers
- Edward Hines, Jr. VA HospitalUS3 papers
- Johns Hopkins UniversityUS3 papers
- Loyola University ChicagoUS3 papers
- National Cancer InstituteUS3 papers
- ProtonVDA (United States)US3 papers
- University College LondonGB3 papers
- University of California San DiegoUS3 papers
- BioLegend (United States)US2 papers
6 papers · 1 filter
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…
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
Yikuan Li, Liang Yao, Chengsheng Mao +3
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will…
Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images
Chengsheng Mao, Yiheng Pan, Zexian Zeng +2
Thoracic diseases are very serious health problems that plague a large number of people. Chest X-ray is currently one of the most popular methods to diagnose thoracic diseases, pla…
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Ye Liu, Lifang He, Bokai Cao +3
Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a…
Are My EHRs Private Enough? -Event-level Privacy Protection
Chengsheng Mao, Yuan Zhao, Mengxin Sun +1
Privacy is a major concern in sharing human subject data to researchers for secondary analyses. A simple binary consent (opt-in or not) may significantly reduce the amount of shara…