The XGBoost-based approach demonstrated robust external validation across multiple centers, supporting clinical adoption to guide personalized treatment decisions.

Using the XGBoost algorithm, we developed a classifier incorporating nine genes (ARHGAP9, CADM1, CPE, DUSP3, FGFR1, GALNT3, IGF2BP3, KIF26A, ZFP3). In our internal cohort, the classifier exhibited ...

A machine learning-based model can predict 30-day in-hospital mortality among patients with asthma in the ICU.

Engineered nanozymes and explainable machine learning enable sensitive bacterial detection across complex conditions. The system uses three distinct signals and delivers transparent, verifiable ...

For the study, "Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System," the researchers used the Bio-Clinical BERT LLM and the XGBoost algorithm ...

To estimate the m-CTSIB scores, researchers applied Multiple Linear Regression, Support Vector Regression and XGBOOST algorithms.