scholarly journals Deep LDL-EHR: Real-time Routine Clinical Application of Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol on Electronic Health Record (Preprint)

10.2196/29331 ◽  
2021 ◽  
Author(s):  
Young Uh
Keyword(s):  
Neural Network ◽  
Electronic Health Record ◽  
Clinical Application ◽  
Deep Neural Network ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Health Record ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol ◽  
Routine Clinical Application

scholarly journals Deep LDL-EHR: Real-time Routine Clinical Application of Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol on Electronic Health Record (Preprint)

2021 ◽  
Author(s):  
Young Uh
Keyword(s):  
Neural Network ◽  
Electronic Health Record ◽  
Real Time ◽  
Clinical Application ◽  
Deep Neural Network ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Health Record ◽  
Low Density ◽  
Electronic Health

BACKGROUND Previously, we constructedWe applied the LDL-DNN model to an electronic health record (EHR) system in real time (deep LDL-EHR). a deep neural network (DNN) model for estimating low-density lipoprotein (LDL) cholesterol (LDL-DNN). OBJECTIVE We applied the LDL-DNN model to an electronic health record (EHR) system in real time (deep LDL-EHR). METHODS The Korea National Health and Nutrition Examination Survey and the Wonju Severance Christian Hospital (WSCH) datasets were used as training and testing datasets, respectively. We measured the model’s performance by using four indices, including bias, root mean square error, P10 to P30, and concordance. For transfer learning (TL), we pre-trained the DNN model using a training dataset, and fine-tuned it using 30% of the testing datasets. RESULTS Based on the four accuracy criteria, the DNN-EHR model generated inaccurate results compared to other methods for LDL-C estimation. By comparing the training and testing datasets, we found there to be an overfitting problem. We revised the LDL-DNN model using the TL algorithms and randomly selected sub-data from the WSCH dataset. As a result, the LDL-DNN-TL model exhibited the best performance among the other methods. CONCLUSIONS The LDL-DNN-TL model is expected to be suitable for routine real-time clinical application for LDL-C estimation in a clinical laboratory.

Deep neural network for estimating low density lipoprotein cholesterol

2019 ◽  
Vol 489 ◽  
pp. 35-40 ◽  
Author(s):  
Taesic Lee ◽  
Juwon Kim ◽  
Young Uh ◽  
Hyunju Lee
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol

Empagliflozin Improves Kidney Outcomes Irrespective of Control of Blood Pressure, Low-Density Lipoprotein Cholesterol and HbA1c

2019 ◽  
Author(s):  
C Wanner ◽  
M Cooper ◽  
S Inzucchi ◽  
B Zinman ◽  
I Zwiener ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol
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