Neural network model outperforms conventional equations in LDL cholesterol estimation: A comparative study of 188,887 Chinese individuals with focus on hypertriglyceridemia
Hao Xue, Yu Lin, Youlin Liu, Lin Li, Pengzhen Chen, Mingyang Li, Ling Ji, Yong Xia · 2025
Read Neural network model outperforms conventional equations in LDL cholesterol estimation: A comparative study of 188,887 Chinese individuals with focus on hypertriglyceridemia by Hao Xue, Yu Lin, Youlin Liu, Lin Li, Pengzhen Chen, Mingyang Li, Ling Ji, Yong Xia (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12702375.txt.
Description
A peer-reviewed research article from Europe PMC (2025). BackgroundAccurate estimation of low-density lipoprotein cholesterol (LDL-C) is crucial for atherosclerotic cardiovascular disease (ASCVD) risk management. Extensive international validation studies have demonstrated that traditional formulas (Friedewald, Martin/Hopkins, Sampson) often yield significant errors under conditions of extreme hypertriglyceridemia.
Catalog record
| CREATOR | Hao Xue, Yu Lin, Youlin Liu, Lin Li, Pengzhen Chen, Mingyang Li, Ling Ji, Yong Xia |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |