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Validation of artificial intelligence algorithm LuxIA for screening of diabetic retinopathy from a single 45° retinal colour fundus images: the CARDS study.

BMJ open ophthalmology
OBJECTIVE: This study validated the artificial intelligence (AI)-based algorithm LuxIA for screening more-than-mild diabetic retinopathy (mtmDR) from a single 45° colour fundus image of patients with diabetes mellitus (DM, type 1 or type 2) in Spain....

Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach.

Epidemiology and psychiatric sciences
AIMS: Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs us...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

PloS one
INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to inconsistent and not mandatory International Classification of Diseases (ICD) codes. We developed an...

Optimizing deep neural networks for high-resolution land cover classification through data augmentation.

Environmental monitoring and assessment
This study presents an innovative approach to high-resolution land cover classification using deep learning, tackling the challenge of working with an exceptionally small dataset. Manual annotation of land cover data is both time-consuming and labor-...

An informed machine learning based environmental risk score for hypertension in European adults.

Artificial intelligence in medicine
BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle with challenges including multicollinearity, non-linearity and confounding. To address these limitation...

A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks.

Scientific reports
The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter es...

Predicting Weight Loss Success After Gastric Sleeve Surgery: A Machine Learning-Based Approach.

Nutrients
BACKGROUND/OBJECTIVES: Obesity is a global health issue, and in this context, bariatric surgery is considered the most effective treatment for severe cases. However, postoperative outcomes vary widely among individuals, driving the development of too...

Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.

BMC medical informatics and decision making
BACKGROUND AND OBJECTIVE: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning models that predict the risk of co...

New perspectives on university quality assessment: A Mamdani Fuzzy Inference System approach.

PloS one
Higher education has traditionally played the role of an overarching factor in economic growth and development. The implementation of the European Higher Education Area (EHEA) has already achieved improvements in many educational areas, but there rem...