Publications by authors named "Gabriel de Maeztu"
Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce.
View Article and Find Full Text PDFBackground: Electronic Clinical Narratives (ECNs) store valuable individual's health information. However, there are few available open-source data. Besides, ECNs can be structurally heterogeneous, ranging from documents with explicit section headings or titles to unstructured notes.
View Article and Find Full Text PDFArticle Synopsis
- The study emphasizes the importance of real world data (RWD) for understanding and responding to the COVID-19 pandemic using a standardized approach through the CHARYBDIS framework.
- Researchers conducted a retrospective database study across multiple countries, including the US and parts of Europe and Asia, involving over 4.5 million individuals and focusing on their clinical characteristics and outcomes.
- Findings reveal higher diagnoses among women but more hospitalizations among men, common comorbidities like diabetes and heart disease, and key symptoms such as cough and fever; this data helps to identify trends in COVID-19 across different populations and time periods.
Objective: To characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.
Design And Setting: This is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.
Article Synopsis
- Routinely collected real-world data (RWD) is essential for understanding and responding to the COVID-19 pandemic, as demonstrated by the CHARYBDIS framework for standardizing and analyzing this data.
- A descriptive cohort study involving over 4.5 million individuals was conducted across the U.S., Europe, and Asia to examine COVID-19-related health risks and outcomes, with detailed information available on an interactive website.
- The findings from the CHARYBDIS study serve as benchmarks to enhance our knowledge of COVID-19's progression and management, facilitating timely evaluations of new preventative and therapeutic strategies.
Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning.
Int J Environ Res Public Health
February 2020
: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. : The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance.
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