A5018421414- Health, Environment, Cognitive Aging
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence in Healthcare
- Cardiovascular Health and Risk Factors
- Machine Learning in Healthcare
- Health disparities and outcomes
- Dementia and Cognitive Impairment Research
- Resilience and Mental Health
- Biomedical and Engineering Education
- Pregnancy and preeclampsia studies
- Blood Pressure and Hypertension Studies
- Digital Mental Health Interventions
- Advanced X-ray and CT Imaging
- Cardiac Imaging and Diagnostics
- Ethics in Clinical Research
- Genetic Associations and Epidemiology
- Nutritional Studies and Diet
- Radiomics and Machine Learning in Medical Imaging
- Health Promotion and Cardiovascular Prevention
- Ethics and Social Impacts of AI
- Cardiovascular Function and Risk Factors
- Environmental Philosophy and Ethics
- Religion, Ecology, and Ethics
- COVID-19 and Mental Health
- Mental Health Research Topics
Universitat de Barcelona
2022-2025
Hospital Sant Joan de Déu Barcelona
2022
Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited clinical practice. This paper describes FUTURE-AI framework, which provides guidance development trustworthy tools healthcare. The Consortium was founded 2021 comprises 117 interdisciplinary experts from 50 countries representing all continents, including scientists, researchers, biomedical ethicists, social scientists. Over a two year period,...
The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged health outcomes are influenced not only by genetic factors but also the interactions between these and various exposures. Consequently, has emerged as a significant contributor to overall risk of developing major diseases, such cardiovascular disease (CVD) diabetes. Therefore, personalized early assessment based on attributes might be promising tool for identifying...
<title>Abstract</title> Mental illnesses affect almost 15% of the world's population, with half cases emerging before age 14. Improved methods for predicting progression mental distress among adolescents, particularly in vulnerable populations, are needed. This study utilized traditional machine learning techniques to predict health status at 17. We assessed correlates outcomes a sample 632 adolescents general (i.e., total difficulties score 17 or higher) 11, who participated UK Millennium...
Abstract Background The temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle influences, and metabolic such diabetes, hypertension. While physiological pathways linking have been investigated, study sex population differences in CMC still not described. Methods We present a machine learning approach to model investigate two...
Abstract Purpose Diagnosing dementia, affecting over 55 million people globally, is challenging and costly, often leading to late-stage diagnoses. This study aims develop early, accurate, cost-effective dementia screening methods using exposome predictors machine learning. We investigate whether low-cost combined with learning models can reliably identify individuals at risk of dementia. Methods analyzed data from 500,000 UK Biobank participants, selecting 1523 diagnosed an equal number...
Diabetes is a high-burden non-communicable disease affecting more than 532 million people worldwide and resulting in range of life-threatening comorbidities. Pre-identifying high-risk individuals applying preventive actions will likely reduce the prevalence health consequences diabetes. Under this context, we developed evaluated first predictive model diabetes risk that combines both electrocardiography (ECG) exposome predictors. A comprehensive list ECG signals variables were extracted from...
Abstract Diagnosing dementia, a syndrome that currently affects more than 55 million people worldwide, remains particularly challenging and costly task. It may involve undertaking several medical tests such as brain scans, cognitive genetic to determine the presence degree of decline. These procedures are associated with long procedures, subjective evaluations high costs. As result, patients often diagnosed at late stage, when symptoms become highly pronounced. Therefore, there is an urgent...
The human exposome is nowadays recognized as a significant contributor to the overall risk of developing major pathologies, such cardiovascular disease (CVD) and diabetes. Therefore, personalized early assessment based on attributes might be promising tool for identifying high-risk individuals improving prevention. In this work, we present novel, fair machine learning (ML) model CVD type 2 diabetes (T2D) prediction set readily available factors. We evaluated our using internal external...