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Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.
BMJ open diabetes research & care
Jun 4, 2024
INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered...
Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.
Cancer immunology, immunotherapy : CII
Jun 4, 2024
BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunother...
A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques.
Frontiers in endocrinology
Jun 4, 2024
PURPOSE: The main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analysis of chest CT scans. An additional aim is to combine these techniques with bone turnover markers to...
Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk.
PloS one
Jun 4, 2024
OBJECTIVE: To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the a...
Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.
Cardiology journal
Jun 4, 2024
IMTRODUCTION: The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) app...
Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.
Journal of magnetic resonance imaging : JMRI
Jun 3, 2024
BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs.
New Model and Public Online Prediction Platform for Risk Stratification of Vocal Cord Leukoplakia.
The Laryngoscope
Jun 3, 2024
OBJECTIVE: To extract texture features from vocal cord leukoplakia (VCL) images and establish a VCL risk stratification prediction model using machine learning (ML) techniques.
Adoption of deep learning-based magnetic resonance image information diagnosis in brain function network analysis of Parkinson's disease patients with end-of-dose wearing-off.
Journal of neuroscience methods
Jun 3, 2024
OBJECTIVE: this study was to analyze the brain functional network of end-of-dose wearing-off (EODWO) in patients with Parkinson's disease (PD) using a convolutional neural network (CNN)-based functional magnetic resonance imaging (fMRI) data classifi...
Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.
Journal of neuroscience methods
Jun 3, 2024
BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize an...
Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.
Diabetes, obesity & metabolism
Jun 3, 2024
AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.
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