Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model.

Autor: Shuaijie Zhang, Fan Yang, Lijie Wang, Shucheng Si, Jianmei Zhang, Fuzhong Xue
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: PLoS Computational Biology, Vol 19, Iss 9, p e1011396 (2023)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1011396&type=printable
Popis: Personalized prediction of chronic diseases is crucial for reducing the disease burden. However, previous studies on chronic diseases have not adequately considered the relationship between chronic diseases. To explore the patient-wise risk of multiple chronic diseases, we developed a multitask learning Cox (MTL-Cox) model for personalized prediction of nine typical chronic diseases on the UK Biobank dataset. MTL-Cox employs a multitask learning framework to train semiparametric multivariable Cox models. To comprehensively estimate the performance of the MTL-Cox model, we measured it via five commonly used survival analysis metrics: concordance index, area under the curve (AUC), specificity, sensitivity, and Youden index. In addition, we verified the validity of the MTL-Cox model framework in the Weihai physical examination dataset, from Shandong province, China. The MTL-Cox model achieved a statistically significant (p
Databáze: Directory of Open Access Journals
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