AIMC Topic: Spain

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A textual dataset of de-identified health records in Spanish and Catalan for medical entity recognition and anonymization.

Scientific data
The advancement of clinical natural language processing systems is crucial to exploit the wealth of textual data contained in medical records. Diverse data sources are required in different languages and from different sites to represent global healt...

Intervention through an intelligent technological platform for socio-emotional development and health promotion in adolescents with Type 1 Diabetes Mellitus (emoTICare): A study protocol for randomized controlled trial.

PloS one
BACKGROUND: During adolescence, individuals face various challenges that require adequate psychosocial and emotional adjustment. Additionally, the presence of a chronic illness during this stage, such as Type 1 Diabetes Mellitus (T1DM), introduces ad...

Measurement, Characterization, and Mapping of COVID-19 Misinformation in Spain: Cross-Sectional Study.

JMIR infodemiology
BACKGROUND: The COVID-19 pandemic has been accompanied by an unprecedented infodemic characterized by the widespread dissemination of misinformation. Globally, misinformation about COVID-19 has led to polarized beliefs and behaviors, including vaccin...

Detection of Negative Emotions in Short Texts Using Deep Neural Networks.

Cyberpsychology, behavior and social networking
Emotion detection is crucial in various domains, including psychology, health, social sciences, and marketing. Specifically, in psychology, identifying negative emotions in short Spanish texts, such as tweets, is vital for understanding individuals' ...

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...

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...

Remdesivir associated with reduced mortality in hospitalized COVID-19 patients: treatment effectiveness using real-world data and natural language processing.

BMC infectious diseases
BACKGROUND: Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (...

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-...

Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.

Parasites & vectors
BACKGROUND: Mosquito-borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region of Spain, changing ...