Even while listening, the brain attempts to anticipate the next words. This is the conclusion reached by a current study conducted by an interdisciplinary team of researchers led by PD Dr. Patrick ...

The team built a DenseNet – a densely connected convolutional neural network – that learns hierarchical features directly ...

Objectives This study aimed to identify a deterioration prediction tool for patients after craniotomy. Design A retrospective cohort study. Setting Three large tertiary hospitals in Hunan Province, ...

Artificial intelligence shows promise for improving care for peripheral artery disease through earlier detection, improved risk stratification, more tailored treatment planning, and more efficient ...

aNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark bDepartment of Public Health, Faculty of Health and Medical ...

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...

ABSTRACT: This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates ...

This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates Random Forest ...

Abstract: This paper analyzes the performance of different LDA combinations with machine learning algorithms in predicting diabetes based on clinical data. The analysis involves patient records with ...

Nocturnal hypoglycemia (NH) is a common adverse event in elderly patients with type 2 diabetes (T2D). This study aims to develop a clinically applicable model for predicting the risk of NH in elderly ...