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A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes - An exploratory analysis.

Primary care diabetes
AIMS: Machine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived with diabetes unknowingly....

Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging.

Neuroradiology
PURPOSE: Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous studies have primarily or exclusively relied on anterior circulation stroke cases to build machine learning (ML) models for predicting onset time. To d...

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

Transplantation
BACKGROUND: Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative...

Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This work focuses on the issue of observer-based resilient dissipativity control of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, the Luenberger observer is designed, and additionally ...

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

Medical physics
BACKGROUND: Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion an...

Development of a machine learning model for prediction of the duration of unassisted spontaneous breathing in patients during prolonged weaning from mechanical ventilation.

Journal of critical care
PURPOSE: Treatment of patients undergoing prolonged weaning from mechanical ventilation includes repeated spontaneous breathing trials (SBTs) without respiratory support, whose duration must be balanced critically to prevent over- and underload of re...

Predefined-time distributed optimization and anti-disturbance control for nonlinear multi-agent system with neural network estimator: A hierarchical framework.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the predefined-time distributed optimization of nonlinear multi-agent system using a hierarchical control approach. Considering unknown nonlinear functions and external disturbances, we propose a two-layer hierarchical control fr...

A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acqu...

Global prediction of extreme floods in ungauged watersheds.

Nature
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simula...

[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening].

Bulletin du cancer
INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of a...