George Vlachogiannis: Machine Learning Model Predicting Cardiac Readmissions in Blood Cancer Patients
George Vlachogiannis, Managing Editor – Cancer Control at Sage, shared an article on LinkedIn:
“Nguyen Le, Chanhyun Park, and colleagues from The University of Texas at Austin report a novel machine learning (ML) model that accurately predicts 90-day unplanned readmissions due to major adverse cardiac events (MACE) in patients with blood cancers.
Using a national hospital database and advanced ML techniques, the authors identified key risk factors—such as older age, heart failure, and coronary disease—and leveraged explainable AI (SHAP) to uncover high-risk patient subgroups.
Findings pave the way for risk-adapted discharge planning and follow-up, helping reduce readmissions, improve outcomes, and ease the healthcare burden.
The study was recently published in Cancer Control.”
Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.
Authors: Nguyen Le, Sola Han, Ahmed S. Kenawy, Yeijin Kim, Chanhyun Park
You can read the full article on Journal of Cancer Control.
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