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.

George Vlachogiannis: Machine Learning Model Predicting Cardiac Readmissions in Blood Cancer Patients

More posts featuring George Vlachogiannis.