José L. Ayala

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Verified

José verified their affiliation via an institutional email.

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• PhD in Computer and Electronic Engineering
• Professor (Full) at Complutense University of Madrid

Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of survival (like genetic alterations or transcriptomic patterns of gene expression) is one of the most relevant fields in current research. Statistical methods and metrics to analyze time-to-event data...

Background Cardiovascular (CV) disease is the leading cause of death in women. Although 80% of CV disease events can be prevented, mortality is projected to increase, particularly in young women. Objectives To promote CV health in women and encourage appropriate lifestyle changes by increasing awareness through vascular ultrasound imaging. Method...

Objective To compare the risk of cardiovascular disease and the occurrence of cardiovascular events in the mid-long term after delivery, between women with and without a history of early-onset preeclampsia. Methods A prospective case-control study has been conducted in Hospital Universitario 12 de Octubre, Madrid. 50 women with early-onset preecla...

Background Non-invasive brain stimulation has shown positive results in maximizing the effects of language therapy in primary progressive aphasia (PPA). Due to the different patterns of brain damage in each aphasia variant, we hypothesized that patients with non-fluent and semantic variants would show a differential response to transcranial magneti...

Immune checkpoint inhibitors (ICIs) have become essential in managing metastatic Renal Cell Carcinoma (mRCC), although selecting the most suitable patients for each specific treatment remains an unmet medical need. In this work, we aimed to create and evaluate a treatment response prediction machine learning model based on tumor gene expression dat...

Developing interfaces for seizure diagnosis, often challenging to detect visually, is rising. However, their effectiveness is constrained by the need for diverse and extensive databases. This study aimed to create a seizure detection methodology incorporating detailed information from each EEG channel and accounts for frequency band variations link...

Blood oxygen saturation (SpO2) is vital for patient monitoring, particularly in clinical settings. Traditional SpO2 estimation methods have limitations, which can be addressed by analyzing photoplethysmography (PPG) signals with artificial intelligence (AI) techniques. This systematic review, following PRISMA guidelines, analyzed 183 unique referen...

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants d...

This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate...

Background The biological definition of Alzheimer’s disease (AD) and the use of biomarkers according to the AT(N) system may improve the clinical characterization of patients and the sequence of events during the clinical course. However, some studies have raised challenges in the clinical application of AT(N) system. Unbiased, data‐driven techniqu...

Graph Theory has spread across different domains due to its ability to capture and exploit information about the interactions among elements. One of the most promising applications relies on the area of Neuroscience, where the exploration of brain connectivity (BC) has become a topic of major interest. In this context, we introduce a new framework...

Background and purpose “Brain fog” is a frequent and disabling symptom that can occur after SARS‐CoV‐2 infection. However, its clinical characteristics and the relationships among brain fog and objective cognitive function, fatigue, and neuropsychiatric symptoms (depression, anxiety) are still unclear. In this study, we aimed to examine the charact...

Aims The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data‐driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. Methods We compared the diagnostic and prognost...

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 minutes of monitoring, to predict the exitus duri...

The increasing transistor scale integration poses, among others, the thermal-aware floorplanning problem; consisting of how to place the hardware components in order to reduce overheating by dissipation. Due to the huge amount of feasible floorplans, most of the solutions found in the literature include an evolutionary algorithm for, either partial...

In the last decade, the Internet of Things paradigm has caused an exponential increase in the number of connected devices. This trend brings the Internet closer to everyday activities and enables data collection that can be used to create and improve a great variety of services and applications. Despite its great benefits, this paradigm also comes...

Background: We aimed to develop objective criteria for cognitive dysfunction associated with the post-COVID syndrome. Methods: Four hundred and four patients with post-COVID syndrome from two centers were evaluated with comprehensive neuropsychological batteries. The International Classification for Cognitive Disorders in Epilepsy (IC-CoDE) fram...

Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the tem...

Background We aimed to develop objective criteria for cognitive dysfunction associated with the post-COVID syndrome. Methods Four hundred and four patients with post-COVID syndrome from two centers were evaluated with comprehensive neuropsychological batteries. The International Classification for Cognitive Disorders in Epilepsy (IC-CoDE) framewor...

Alzheimer's disease (AD) is a neurodegenerative disease whose molecular mechanisms are activated several years before cognitive symptoms appear. Genotype-based prediction of the phenotype is thus a key challenge for the early diagnosis of AD. Machine learning techniques that have been proposed to address this challenge do not consider known biologi...

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients’ evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a comput...

Fatigue is one of the most disabling symptoms in several neurological disorders and has an important cognitive component. However, the relationship between self-reported cognitive fatigue and objective cognitive assessment results remains elusive. Patients with post-COVID syndrome often report fatigue and cognitive issues several months after the a...

Background Early onset preeclampsia (eoPE) is a hypertensive disorder of pregnancy with endothelial dysfunction manifested before 34 weeks where expectant management is usually attempted. However, the timing of hospitalization, corticosteroids, and delivery remain a challenge. We aim to develop a prediction model using machine-learning tools for th...

The Internet of Things (IoT) has caused an exponential increase in the number of connected devices. This brings the Internet closer to everyday activities and enables data collection that can be used to create and improve a great variety of services. However, more powerful storage and processing capabilities are required to service all these device...

Objective: Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are two distinct degenerative disorders with overlapping genetics, clinical manifestations, and pathology, including the presence of TDP-43 aggregates in nearly 50% of patients with FTD and 98% of all patients with ALS. Here, we evaluate whether different genetically p...

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by select...

Data centers are power hungry facilities. Energy-aware task scheduling approaches are of utmost importance to improve energy savings in data centers, although they need to know beforehand the energy consumption of the applications that will run in the servers. This is usually done through a full profiling of the applications, which is not feasible...

Background Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning...

Background Diagnosis of Alzheimer’s disease and behavioral variant frontotemporal dementia is often challenging. In spite of comprehensive clinical and cognitive assessments, the use of biomarkers is usually needed. We aimed to develop machine learning based models for the diagnosis of AD and bvFTD using only cognitive testing. These techniques may...

Background Genetic algorithms are methods used in machine learning, which have a proven capability to explore a large space of solutions, deal with very large numbers of input features, and avoid local minima. Diagnosis of Alzheimer’s Disease (AD) and Frontotemporal dementia (FTD) is often challenging, and thorough costly assessments are often need...

Chronic diseases benefit of the advances on personalize medicine coming out of the integrative convergence of significant developments in systems biology, the Internet of Things and Artificial Intelligence. 70% to 80% of all healthcare costs in the EU and US are currently spent on chronic diseases, leading to estimated costs of ${\rm C}\!\!\!\!\!\...

Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients cate...

Background Primary progressive aphasia (PPA) is a neurodegenerative syndrome for which no effective treatment is available. Objective We aimed to assess the effect of repetitive transcranial magnetic stimulation (rTMS), using personalized targeting. Methods We conducted a randomized, double-blind, pilot study of patients with PPA receiving rTMS,...

Background Primary progressive aphasia (PPA) is a neurodegenerative syndrome for which no effective treatment is available. We aimed to assess the effect of repetitive transcranial magnetic stimulation (rTMS), using personalized targeting. Methods We conducted a randomized, double-blind, pilot study of patients with PPA receiving rTMS, with a subgr...

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 min of monitoring, to predict the exitus during t...

Background Primary progressive aphasia (PPA) is a neurodegenerative syndrome characterized by the neurodegeneration of language brain regions and networks. As in many other neurodegenerative disorders, effective treatments are lacking or have limited efficacy. Repetitive transcranial magnetic stimulation (rTMS) has been proposed as a potential trea...

Chronic diseases represent the major health problems of the twenty-first century. These diseases kill 41 million people each year, equivalent to 71% of all deaths globally. The major chronic diseases listed by the World Health Organization are cardiovascular diseases, cancer, chronic respiratory diseases, diabetes mellitus, and neurodegenerative di...

This work deals with the improvement of multi-target prediction models through a proposed optimization called Selection Of medical Features by Induced Alterations in numeric labels (SOFIA). This method performs a data transformation when: (1) weighting the features, (2) performing small perturbations on numeric labels and (3) selecting the features...

The computation power in data center facilities is increasing significantly. This brings with it an increase of power consumption in data centers. Techniques such as power budgeting or resource management are used in data centers to increase energy efficiency. These techniques require to know beforehand the energy consumption throughout a full prof...

Background: To develop and validate a novel, machine learning-derived model for prediction of cardiovascular (CV) mortality risk using office (OBP) and ambulatory blood pressure (ABP), to compare its performance with existing risk scores, and to assess the possibility of predicting ABP phenotypes (i.e. white-coat, ambulatory and masked hypertension...

Prediction of symptomatic crises in chronic diseases allows doctor or patient to take important decisions before the symptoms occur, like the intake of drugs to avoid symptoms or the activation of alarms. Objective and accurate prediction is necessary to increase the effectiveness of the health system. Each disease is different in nature, but the p...

The demand of novel IoT and smart city applications is increasing significantly and it is expected that by 2020 the number of connected devices will reach 20.41 billion. Many of these applications and services manage real-time data analytics with high volumes of data, thus requiring an efficient computing infrastructure. Edge computing helps to ena...

Background: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a par...

In this paper, an alternative to the traditional methodology related to signal processing-like subjects is proposed. These are subjects that require a deep mathematical and theoretical basis, but the practical goal is not often emphasized, which drives students to lose interest in the subject. Thus, a software-defined radio environment is proposed...

Deciding on continuous treatment of chronic diseases is vital in terms of economy, quality of life and time. We present a holistic data mining approach that addresses the prediction of the therapeutic response in a panoramic and feedback way, while unveiling relevant medical factors. Panoramic prediction makes it possible to decide whether the trea...

Introduction Primary progressive aphasia (PPA) is a clinical syndrome of neurodegenerative origin with 3 main variants: non-fluent, semantic, and logopenic. However, there is some controversy about the existence of additional subtypes. Our aim was to study the language and cognitive features associated with a new proposed classification for PPA. M...

The application of computer-aided techniques regarding stroke diagnosis is especially important in non-urban areas, because of the lack of adequate resources. The purpose of this research consists of testing the hypothesis that state-of-the-art machine learning-based modeling techniques, when integrated with non-invasive monitoring technologies, ca...

he application of computer-aided techniques regarding stroke diagnosis is especially important in non-urban areas, because of the lack of adequate resources. The purpose of this research consists of testing the hypothesis that state-of-the-art machine learning-based modeling techniques, when integrated with non-invasive monitoring technologies, can...

Migraine affects the daily life of millions of people around the world. The most well-known disabling symptom associated with this illness is the intense headache. Nowadays, there are treatments that can diminish the level of pain. OnabotulinumtoxinA (BoNT-A) has become a very popular medication for treating migraine headaches in those cases in whi...

Purpose Premonitory symptoms (PSs) of migraine are those that precede pain in a migraine attack. Previous studies suggest that treatment during this phase may prevent the onset of pain; however, this approach requires that patients be able to recognize their PSs. Our objectives were to evaluate patients’ actual ability to predict migraine attacks b...

Background. Primary progressive aphasia (PPA) is a clinical syndrome characterized by the neurodegeneration of language brain systems. Three main clinical forms (non-fluent, semantic, and logopenic PPA) have been recognized, but applicability of the classification and the capacity to predict the underlying pathology is controversial. We aimed to st...

Migraine is one of the most disabling neurological diseases. Its prevalence reaches 15% of the population in developed countries and lead to high economic costs for private and national health systems. There is no cure for the migraine yet. Objective and accurate migraine prediction turns necessary in order to increase the effectiveness of the curr...

Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the...

Renewable energies, in particular wind energy, are characterized as highly variable and unpredictable in terms of production, and they are increasingly more important in the context of the smart grid energy production. In this scenario, accurate prediction models and techniques are desirable to optimize the renewable energy production and reduce th...

At the end of 2017, Vijaykrishnan Nayaranan, Distinguished Professor of Computer Science and Engineering and Electrical Engineering at Pennsylvania State University, USA, completed his four-year service as the Editor-in-Chief (EIC) for the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ( TCAD ). Vijay has not only...

The Internet of Things (IoT) holds big promises for healthcare, especially in proactive personal eHealth. Prediction of symptomatic crises in chronic diseases in the IoT scenario leads to the deployment of ambulatory monitoring systems. These systems place a major concern in the amount of data to be processed and the intelligent management of the e...

The 2017 edition of the International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design (SMACD 2017) was held at Giardini Naxos-Taormina, Italy, on 12–15 June 2017, with the technical sponsorships of the IEEE, IEEE Circuits and Systems Society, and the IEEE Council on Electronic Design Automation...

Isaac Newton is reported to have said in 1676: “If I have seen further, it is by standing on the shoulders of giants.” IEEE offers you another such opportunity in 2017.

Three-dimensional network-on-chip systems are getting popular among the integrated circuit (IC) manufacturer because of reduced latency, heterogeneous integration of technologies on a single chip, high yield, and consumption of less interconnecting power. However, the addition of functional units in the Z-direction has resulted in higher on-chip te...

In the Internet of Things (IoT) era, there is growing interest in wireless monitoring sensors for detection, classification and prediction of health symptoms. The prediction of symptoms in chronic diseases such as migraines brings new hope to improve patients' lives. The prediction of a migraine symptomatic event through monitoring hemodynamic vari...

Code Ocean is a cloud-based executable research platform that allows authors to share their algorithms in an effort to make the world’s scientific code more open and reproducible. Uploading your algorithms and associated data files to the Code Ocean site is easy. Anyone can run an algorithm posted to Code Ocean, modify it, and test the modification...

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The use of information and communication technologies (ICTs) to improve the quality of life of people with chronic and degenerative diseases is a topic receiving much attention nowadays. We can observe that new technologies have driven numerous scientific projects in e-Health, encompassing Smart and Mobile Health, in order to address all the matter...

Managing energy efficiency under timing constraints is an interesting and big challenge. This work proposes an accurate power model in data centers for time-constrained servers in Cloud computing. This model, as opposed to previous approaches, does not only consider the workload assigned to the processing element, but also incorporates the need of...

Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with the battery autonomy of the devices. Several research areas are focusing their efforts to reduce the energetic impact in these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimizatio...

With 3D NoCs help improve circuit performance, fault tolerance and energy efficiency through the reduction of average wire-length and the increase in communication bandwidth of on-chip wiring, the soaring increase of on-chip temperature remains one of the most challenging obstacles to their commercialization. We present a physical design flow that...

Computational demand in data centers is increasing because of the growing popularity of Cloud applications. However, data centers are becoming unsustainable in terms of power consumption and growing energy costs so Cloud providers have to face the major challenge of placing them on a more scalable curve. Also, Cloud services are provided under stri...

The migraine disease is one of the most disabling neurological diseases that negatively impacts on the quality of life and on the cost of the public health services. The prediction of a migraine symptomatic event through monitorization of hemodynamic variables has been previously demonstrated in our previous works. In this paper, a first approach f...

Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the tem...

Design and development of hard Real-Time (RT) embedded systems present several crucial requirements regarding criticality and timeliness of these systems. Formal methods have been presented as a promising alternative to deal with the design issues of these applications. However, these formal method do not scale well in complex systems. Modeling and...

3D network-on-chip (NoC) has emerged as a cutting edge technology that provides better performance by combining features of NoC and die-stacking IC technology. It is able to push the limits of Moores law by increasing the density of components in a chip resulting in higher functionality. The increasing packing density and power consumption of syste...

The migraine disease is a chronic headache presenting symptomatic crisis that causes high economic costs to the national health services, and impacts negatively on the quality of life of the patients. Even if some patients can feel unspecific symptoms before the onset of the migraine, these only happen randomly and cannot predict the crisis precise...