ArticleJMIR medical informatics2021
Retracted: A Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol From Electronic Health Records: Real-Time Routine Clinical Application.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Retraction: A Deep Neural Network for Estimating Low-Density Lipoprotein Cholesterol From Electronic Health Records: Real-Time Routine Clinical Application.JMIR medical informatics · 2026Article
- Leveraging AI and transfer learning to enhance out-of-hospital cardiac arrest outcome prediction in diverse setting.NPJ digital medicine · 2025Article
- Medication-Stratified Analysis of LDL-C Equation Miscalibration in Diabetes: Evidence from the All of Us Research Program and a Medication-Agnostic Machine-Learning Correction.medRxiv : the preprint server for health sciences · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices.Journal of clinical medicine · 2023Article
- Prediction Model for Pre-Eclampsia Using Gestational-Age-Specific Serum Creatinine Distribution.Biology · 2023Article
- Gestational age-specific serum creatinine can predict adverse pregnancy outcomes.Scientific reports · 2022Article
- Identification of the robust predictor for sepsis based on clustering analysis.Scientific reports · 2022Article
- Comparison of a Machine Learning Method and Various Equations for Estimating Low-Density Lipoprotein Cholesterol in Korean Populations.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPreviously, we constructed a deep neural network (DNN) model to estimate low-density lipoprotein cholesterol (LDL-C).
objectiveTo routinely provide estimated LDL-C levels, we applied the aforementioned DNN model to an electronic health record (EHR) system in real time (deep LDL-EHR).
methodsThe 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 our proposed model's performance by using 5 indices, including bias, root mean-square error, P10-P30, concordance, and correlation coefficient. For transfer learning (TL), we pretrained the DNN model using a training dataset and fine-tuned it using 30% of the testing dataset.
resultsBased on 5 accuracy criteria, deep LDL-EHR generated inaccurate results compared with other methods for LDL-C estimation. By comparing the training and testing datasets, we found an overfitting problem. We then revised the DNN model using the TL algorithms and randomly selected subdata from the WSCH dataset. Therefore, the revised model (DNN+TL) exhibited the best performance among all methods.
conclusionsOur DNN+TL is expected to be suitable for routine real-time clinical application for LDL-C estimation in a clinical laboratory.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.