This paper is in the following e-collection/theme issue:

Artificial Intelligence (2310) Clinical Information and Decision Making (2045) Machine Learning (2203) Innovations and Technology in Cancer Care (565)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49016, first published .
Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study

Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study

Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study

Authors of this article:

Jinghui Zhang1, 2, 3 Author Orcid Image ;   Guiyuan Ma1, 2 Author Orcid Image ;   Sha Peng1, 2 Author Orcid Image ;   Jianmei Hou1 Author Orcid Image ;   Ran Xu1, 2 Author Orcid Image ;   Lingxia Luo1, 2 Author Orcid Image ;   Jiaji Hu1, 2 Author Orcid Image ;   Nian Yao1, 2 Author Orcid Image ;   Jiaan Wang4 Author Orcid Image ;   Xin Huang5 Author Orcid Image
Article Authors Cited by (13) Tweetations (3) Metrics

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  9. Lee S, Park J. Risk factors for complications associated with peripherally inserted central venous catheters for parenteral nutrition: Machine learning and survival analysis based on deep learning. Clinical Nutrition 2025;55:249 View
  10. Hu Y, Lang Y, Shen L, Yan L. Development and validation of a nomogram for predicting unplanned PICC removal in preterm infants with gestational age <32 weeks. Frontiers in Pediatrics 2026;14 View
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  12. Zhu B, Xu A, Han T, Chen Q, Zheng A, Chen J. COL8A1 protein molecule drives epithelial-mesenchymal transition in ovarian cancer through the PI3K/AKT axis by using electrochemical sensors and comprehensive molecular subtype analysis. Microchemical Journal 2026;224:117592 View

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