Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 2, 2021
Open Peer Review Period: Apr 1, 2021 - Apr 8, 2021
Date Accepted: Jul 5, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Deep LDL-EHR: Real-time Routine Clinical Application of Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol on Electronic Health Record
ABSTRACT
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.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.