Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks

no code implementations • 10 Apr 2019 • Zhen-Xing Xu, Jingyuan Chou, Xi Sheryl Zhang, Yuan Luo, Tamara Isakova, Prakash Adekkanattu, Jessica S. Ancker, Guoqian Jiang, Richard C. Kiefer, Jennifer A. Pacheco, Luke V. Rasmussen, Jyotishman Pathak, Fei Wang

Sub-phenotype III is with average age 65. 07$ \pm 11. 32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1. 69\pm 0. 32$ mg/dL, eGFR $93. 97\pm 56. 53$ mL/min/1. 73$m^2$).

Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study

no code implementations • 2 Apr 2019 • Prakash Adekkanattu, Guoqian Jiang, Yuan Luo, Paul R. Kingsbury, Zhen-Xing Xu, Luke V. Rasmussen, Jennifer A. Pacheco, Richard C. Kiefer, Daniel J. Stone, Pascal S. Brandt, Liang Yao, Yizhen Zhong, Yu Deng, Fei Wang, Jessica S. Ancker, Thomas R. Campion, Jyotishman Pathak

While the NLP system showed high precision and recall measurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.