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June 22, 2024Asia-Pacific Journal of Oncology NursingOpen Access

Machine learning risk prediction model for bloodstream infections related to totally implantable venous access ports in patients with cancer

Fan Wang Yanyi Zhu Lijuan Wang Caiying Huang Ranran Mei Li-e Deng Xiulan Yang Yan Xu Lingling Zhang Min Xu

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Abstract

This study aimed to develop and validate a machine learning-based risk prediction model for catheter-related bloodstream infection (CRBSI) following implantation of totally implantable venous access ports (TIVAPs) in patients.

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Cite This Study

Wang et al. (2024) studied this question.

synapsesocial.com/papers/68e63af7b6db6435875cd0bd https://doi.org/10.1016/j.apjon.2024.100546

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Analysis of risk factors for bloodstream infections related to totally implantable venous access ports in breast cancer patientsRetrospective Study · 2026
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4Artificial intelligence-driven prediction and interpretation of central line-associated bloodstream infections in ICU: insights from the MIMIC-IV database2025 · 4 citations
5Models for predicting the risk of bloodstream infections associated with peripherally inserted central venous catheters: A scoping review2025 · 2 citations

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