3 citations · 6 across the 5 of their papers we have counts for
5 papers
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records
Jun Wen, Jue Hou, Clara-Lea Bonzel +14
Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily a…
ssROC: Semi-Supervised ROC Analysis for Reliable and Streamlined Evaluation of Phenotyping Algorithms
Jianhui Gao, Clara-Lea Bonzel, Chuan Hong +3
High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medi…
Harnessing electronic health records for real-world evidence
Jue Hou, Rachel Zhao, Jessica Gronsbell +13
While randomized controlled trials (RCTs) are the gold-standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world…
High-throughput relation extraction algorithm development associating knowledge articles and electronic health records
Yucong Lin, Keming Lu, Yulin Chen +2
Objective: Medical relations are the core components of medical knowledge graphs that are needed for healthcare artificial intelligence. However, the requirement of expert annotati…
A Fast Divide-and-Conquer Sparse Cox Regression
Yan Wang, Nathan Palmer, Qian Di +3
We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size is exceedin…