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September 1, 2024Risk Management and Healthcare PolicyOpen Access

Predictive Model of Internal Bleeding in Elderly Aspirin Users Using XGBoost Machine Learning

Tenggao Chen Wanlin Lei Wang Maofeng

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Abstract

This study aimed to develop a predictive model for assessing internal bleeding risk in elderly aspirin users using machine learning.

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Cite This Study

Chen et al. (2024) studied this question.

synapsesocial.com/papers/68e59d79b6db643587537659 https://doi.org/10.2147/rmhp.s478826

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

1LASSO-derived model for the prediction of bleeding in aspirin users2024 · 8 citations
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4Development and validation of a novel bleeding risk prediction tool for aspirin users with a low body mass indexcohort · 2025 · 2 citations
5Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients2024 · 11 citations

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