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Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine

Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine

based on time frames of electronic medical records (2013-2014 for derivation and 2014-2015 for validation).Figure 2Formula of a tree ensemble model developed with the training subset.Figure 3Sum of the loss function and the overfitting control term at the t

Shiying Hao, Tianyun Fu, Qian Wu, Bo Jin, Chunqing Zhu, Zhongkai Hu, Yanting Guo, Yan Zhang, Yunxian Yu, Terry Fouts, Phillip Ng, Devore S Culver, Shaun T Alfreds, Frank Stearns, Karl G Sylvester, Eric Widen, Doff B McElhinney, Xuefeng B Ling

JMIR Med Inform 2017;5(3):e21


Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

The model at the t- th iteration was trained to minimize the following objective, where l is a differentiable convex loss function that not only measures the difference between the target yi and the prediction ŷi(t-1) of the i-th instance at the t-1- th iteration

Chengyin Ye, Tianyun Fu, Shiying Hao, Yan Zhang, Oliver Wang, Bo Jin, Minjie Xia, Modi Liu, Xin Zhou, Qian Wu, Yanting Guo, Chunqing Zhu, Yu-Ming Li, Devore S Culver, Shaun T Alfreds, Frank Stearns, Karl G Sylvester, Eric Widen, Doff McElhinney, Xuefeng Ling

J Med Internet Res 2018;20(1):e22