Publications by authors named "Jose A Gonzalez-Novoa"

Background: The global pandemic caused by SARS-CoV-2 has resulted in millions of people experiencing long COVID condition, a range of persistent symptoms following the acute phase, with an estimated prevalence of 27%-64%.

Materials And Methods: To understand its pathophysiology, we conducted a longitudinal study on viral load and cytokine dynamics in individuals with confirmed SARS-CoV-2 infection. We used reverse transcriptase droplet digital PCR to quantify viral RNA from nasopharyngeal swabs and employed multiplex technology to measure plasma cytokine levels in a cohort of people with SARS-CoV-2 infection.

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Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies.

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Transcatheter aortic valve implantation (TAVI) is a procedure to treat severe aortic stenosis. There are several clinical concerns related to potential complications after the procedure, which demand the analysis of computerized tomography (CT) scans after TAVI to assess the implant's result. This work introduces a novel, fully automatic method for the analysis of post-TAVI 4D-CT scans to characterize the prosthesis and its relationship with the patient's anatomy.

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It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today's hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients' care.

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Alzheimer's disease (AD) is the most common form of dementia. An increasing number of studies have confirmed epigenetic changes in AD. Consequently, a robust phenotyping mechanism must take into consideration the environmental effects on the patient in the generation of phenotypes.

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Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to select appropriate treatment options and to predict patients' conditions. There has been a high amount of data collected by biomedical sensors during the continuous monitoring process of patients in the ICU, so the use of artificial intelligence techniques in automatic LoS estimation would improve patients' care and facilitate the work of healthcare personnel. In this work, a novel methodology to estimate the LoS using data of the first 24 h in the ICU is presented.

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Continuous monitoring of arterial blood pressure (ABP) of patients in hospital is currently carried out in an invasive way, which could represent a risk for them. In this paper, a noninvasive methodology to optimize ABP estimators using electrocardiogram and photoplethysmography signals is proposed. For this, the XGBoost machine learning model, optimized with Bayesian techniques, is executed in a Graphics Processing Unit, which drastically reduces execution time.

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Transcatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The automatic interpretation and parameter extraction on such images can lead to significative improvements and new applications in the procedure that, in most cases, rely on a prior identification of the transcatheter heart valve (THV).

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Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed.

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