Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study
Yuan Fei, Siwei Liu, Tianlang Tong, Xuemei Zhang, Hui Wang, Jun Liu, Xiaoqi Zheng · 2026
Read Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study by Yuan Fei, Siwei Liu, Tianlang Tong, Xuemei Zhang, Hui Wang, Jun Liu, Xiaoqi Zheng (2026) 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/PMC13323579.txt.
Description
Yuan Fei and co-authors build machine learning models to predict complications of high blood pressure, in a 2026 UK Biobank longitudinal study of 502,166 participants. Protein profiling used the Olink Explore platform, and clinical variables plus outcomes came from electronic health records.
During follow-up, heart, brain and kidney complications developed in 1,232, 166 and 549 participants in their counts. Joint clinical and protein models reached fair to good discrimination, strongest for brain complications. Single proteins stood out for each organ outcome, led by growth differentiation factor 15 for heart events. Interpretation tools explained which inputs drove predictions. The authors present the models as an early warning route for targeted follow-up.
Catalog record
| CREATOR | Yuan Fei, Siwei Liu, Tianlang Tong, Xuemei Zhang, Hui Wang, Jun Liu, Xiaoqi Zheng |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55894 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |