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AI-based Prediction of Catheter-related Thrombosis Risk for Cancer Patients

Published: 13 May 2024 Publication History

Abstract

Cancer patients face a heightened risk of venous thromboembolism (VTE), emerging as the second most prevalent cause of death within this population. Central venous catheterization (CVC), a routine procedure in cancer care, amplifies the VTE risk, leading to catheter-related thrombosis (CRT). Although traditional risk-assessment models and certain AI methods exist for VTE prediction, their capability and application in CRT risk prediciton for cancer patients remains limited.
This paper addresses the shortcomings of current models (RAMs) by crafting a dedicated AI model to predict CRT risks for cancer patients. Leveraging a dataset encompassing 10,512 cancer patients undergoing catheterization over a decade, we meticulously select nine specific features for model construction, resulting in an impressive 0.794 AUROC in prediction, 54.9% higher than baseline. Furthermore, we estimate CRT-free probability using the Kaplan-Meier method. We also develop a WeChat Mini Program designed for efficient data collection and risk prediction, enhancing the efficiency of CRT risk detection for both doctors and patients.

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      cover image ACM Conferences
      WWW '24: Companion Proceedings of the ACM Web Conference 2024
      May 2024
      1928 pages
      ISBN:9798400701726
      DOI:10.1145/3589335
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      Publication History

      Published: 13 May 2024

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      Author Tags

      1. catheterization
      2. machine learning
      3. venous thromboembolism

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      Funding Sources

      • CAMS Innovation Fund for Medical Sciences (CIFMS) (supported by the Special Research Fund for Central Universities, Peking Union Medical College)

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      WWW '24
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      WWW '24: The ACM Web Conference 2024
      May 13 - 17, 2024
      Singapore, Singapore

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      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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