Patients’ financial resources affect their enrollment in oncology clinical trials to a greater degree than traditional ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Introduction You are tasked to lead a 40-truck convoy resupplying combat troops at the front during large-scale combat ...

Objective: To develop an auxiliary diagnostic tool for schizophrenia based on multiple test variables using different machine learning algorithms. Results: Arg, TP, ALP, HDL, UA, and LDL were ...

Abstract: This study addresses the lack of comprehensive evaluations of feature scaling by systematically assessing 12 techniques, including less common methods such as VAST and Pareto, in 14 machine ...

Understanding the derivative of the cost function is key to mastering logistic regression. Learn how gradient descent updates weights efficiently in machine learning. #MachineLearning ...

ABSTRACT: Postoperative nausea and vomiting (PONV) is a common complication after anesthesia and surgery. Traditional predictive models, such as Apfel scores, rely on linear assumptions and limited ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

Feasibility and Acceptability of Collecting Passive Smartphone Data for Potential Use in Digital Phenotyping Among Family Caregivers and Patients With Advanced Cancer This study applied three ...

Introduction: This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG).