In this paper, we propose a novel Prediction Model to Study the Impact of Lymphopenia Kinetics on Survival Outcomes in HNSCC Via an Ensemble Tree-Based Machine Learning Approach (POTOMAC). Our main ...

The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models ...

A new study leveraged machine learning (ML) to predict 5-year postoperative survival in patients with stage III colorectal cancer (CRC), identifying key clinical and demographic factors that ...

The machine learning model had better performance than the conventional models and the random forest model best predicted the short-term survival of ACLF patients following liver transplant.

The machine learning algorithms were developed and tested on nearly 10,000 cases of OHCA that happened in Chicago's 77 neighborhoods between 2014 and 2019.

A single blood sample from a critically ill COVID-19 patient can be analyzed by a machine learning model which uses blood plasma proteins to predict survival, weeks before the outcome, according ...

Blood samples collected from patients with severe COVID-19 can be analyzed by a machine learning approach to predict whether they will recover and survive or die from the disease, a PLOS Digital ...