Researchers have developed a machine learning model that shows promising results in predicting the risk of critical bleeding in patients with immune thrombocytopenia (ITP).
By analyzing 10 simple and readily available predictors, the model provides accurate predictions and could potentially support clinicians in making informed decisions
regarding patient management and reducing adverse outcomes. The study, conducted on a large cohort of ITP patients, highlights the significance of machine learning in improving
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