Yang, J., Wan, J., Feng, L., Hou, S., Yv, K., Xu, L., Chen, K., the National Natural Science Foundation of China, the Chongqing Technology Innovation project, & the National Science and Technology Support Plan. (2024). Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis. BMC Medical Informatics and Decision Making ; Volume 24, Issue 1 ; ISSN 1472-6947. https://doi.org/10.1186/s12911-023-02412-z

YANG, Jie, WAN, Jingfang, FENG, Lei, HOU, Shihui, YV, Kaizhen, XU, Liang, CHEN, Kehong, THE NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, THE CHONGQING TECHNOLOGY INNOVATION PROJECT und THE NATIONAL SCIENCE AND TECHNOLOGY SUPPORT PLAN, 2024. Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis. BMC Medical Informatics and Decision Making ; volume 24, issue 1 ; ISSN 1472-6947. 1 Januar 2024. DOI 10.1186/s12911-023-02412-z.

Yang, J., J. Wan, L. Feng, S. Hou, K. Yv, L. Xu, K. Chen, the National Natural Science Foundation of China, the Chongqing Technology Innovation project, und the National Science and Technology Support Plan. „Machine Learning Algorithms for the Prediction of Adverse Prognosis in Patients Undergoing Peritoneal Dialysis“. BMC Medical Informatics and Decision Making ; Volume 24, Issue 1 ; ISSN 1472-6947, Januar 2024, https://doi.org/10.1186/s12911-023-02412-z.

Yang, Jie/Wan, Jingfang/Feng, Lei/Hou, Shihui/Yv, Kaizhen/Xu, Liang u. a.: Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis, BMC Medical Informatics and Decision Making ; volume 24, issue 1 ; ISSN 1472-6947 2024,

Yang, J., Wan, J., Feng, L., Hou, S., Yv, K., Xu, L., Chen, K., the National Natural Science Foundation of China, the Chongqing Technology Innovation project und the National Science and Technology Support Plan. (2024), „Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis“, BMC Medical Informatics and Decision Making ; Volume 24, Issue 1 ; ISSN 1472-6947, verfügbar unter:https://doi.org/10.1186/s12911-023-02412-z.