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Background and objective: Mode of delivery is one of the issues that most concerns obstetricians. The caesarean section rate has increased progressively in recent years, exceeding the limit recommended by health institutions. Obstetricians generally lack the necessary technology to help them decide whether a caesarean delivery is appropriate based on antepartum and intrapartum conditions. Methods: In this study, we have tested the suitability of using three popular artificial intelligence algorithms, Support Vector Machines, Multilayer Perceptron and, Random Forest, to develop a clinical decision support system for the prediction of the mode of delivery according to three categories: caesarean section, euthocic vaginal delivery and, instrumental vaginal delivery. For this purpose, we used a comprehensive clinical database consisting of 25038 records with 48 attributes of women who attended to give birth at the Service of Obstetrics and Gynaecology of the University Clinical Hospital "Virgen de la Arrixaca" in the Murcia Region (Spain) from January of 2016 to January 2019. Women involved were patients with singleton pregnancies who attended to the emergency room on active labour or undergoing a planned induction of labour for medical reasons. Results: The three implemented algorithms showed a similar performance, all of them reaching an accuracy equal to or above 90% in the classification between caesarean and vaginal deliveries and somewhat lower, around 87% between instrumental and euthocic. Conclusions: The results validate the use of these algorithms to build a clinical decision system to help gynaecologists to predict the mode of delivery.

Objective: Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow limitation to the lungs and has a high morbidity around the world. The objective of this study was to evaluate how artificial intelligence (AI) is being applied for the management of the disease, analyzing the objectives that are raised, the algorithms that are used and what results they offer. Methods: We conducted a scoping review following the Arksey and O'Malley (2005) and Levac et al. (2010) guidelines. Two reviewers independently searched, analyzed and extracted data from papers of five databases: Web of Science, PubMed, Scopus, Cinahl and Cochrane. To be included, the studies had to apply some AI techniques for the management of at least one stage of the COPD clinical process. In the event of any discrepancy between both reviewers, the criterion of a third reviewer prevailed. Results: 380 papers were identified through database searches. After applying the exclusion criteria, 67 papers were included in the study. The studies were of a different nature and pursued a wide range of objectives, highlighting mainly those focused on the identification, classification and prevention of the disease. Neural nets, support vector machines and decision trees were the AI algorithms most commonly used. The mean and median values of all the performance metrics evaluated were between 80% and 90%. Conclusions: The results obtained show a growing interest in the development of medical applications that manage the different phases of the COPD clinical process, especially predictive models. According to the performance shown, these models could be a useful complementary tool in the decision-making by health specialists, although more high-quality ML studies are needed to endorse the findings of this study.

Ceramic materials based on naturally occurring clays are a low cost and environmentally friendly alternative to commercial polymer-based membranes in bioelectrochemical systems. In this work, ceramic membranes containing different amounts of iron oxide (1.06, 2.76 and 5.75 vol.%) and sintered at different temperatures (1100, 1200 and 1300 °C) have been elaborated and tested as separators in urine-fed microbial fuel cells (MFCs). The results reveal that the presence of iron oxide in the ceramic membrane composition increases the structural porosity and reduces the pore size for the three temperatures investigated. On the other hand, it was also observed that the iron content mitigates the negative effect of the high sintering temperature on the power performance of the MFCs. In the case of the ceramic membranes sintered at 1300 °C, power output improved ca. 10-fold when the iron oxide content in the membrane increased from 1.06 up to 5.75 vol.% (30.9 and 286.6 µW, respectively). Amongst the different combinations of iron phase content and sintering temperatures, the maximum power output was obtained by MFCs working with separators containing 5.75 vol. % of iron oxide and sintered at 1100 °C (1.045 mW). Finally, the system was stable for 65 days, which supports the long-term functionality of the different materials assessed.

Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology.

Business Process Management is a new strategy for process management that is having a major impact today. Mainly, its use is focused on the industrial, services, and business sector. However, in recent years, it has begun to apply for optimizing clinical processes. So far, no studies that evaluate its true impact on the healthcare sector have been found. This systematic review aims to assess the results of the application of Business Process Management methodology on clinical processes, analyzing whether it can become a useful tool to improve the effectiveness and quality of processes. We conducted a systematic literature review using ScienceDirect, Web of Science, Scopus, PubMed, and Springer databases. After the electronic search process in different databases, 18 articles met the pre-established requirements. The findings support the use of Business Process Management as an effective methodology to optimize clinical processes. Business Process Management has proven to be a feasible and useful methodology to design and optimize clinical processes, as well as to automate tasks. However, a more comprehensive follow-up of this methodology, better technological support, and greater involvement of all the clinical staff are factors that play a key role for the development of its true potential.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease with a high global prevalence. The main scientific societies dedicated to the management of this disease have published clinical practice guidelines for quality practice. However, at present, there are important weaknesses in COPD diagnosis criteria that often lead to underdiagnosis or misdiagnosis. Objective: We sought to develop a new support system for COPD diagnosis. The system was designed to overcome the weaknesses detected in current guidelines with the goals of enabling early diagnosis, and improving the diagnostic accuracy and quality of care provided. Methods: We first analyzed the main clinical guidelines for COPD to detect weaknesses that exist in the current diagnostic process, and then proposed a redesign based on a business process management (BPM) strategy for its optimization. The BPM system acts as a backbone throughout the process of COPD diagnosis in this proposed approach. The newly developed support system was integrated into a health information system for validation of its use in a hospital environment. The system was qualitatively evaluated by experts (n=12) and patients (n=36). Results: Among the 12 experts, 10 (83%) positively evaluated our system with respect to increasing the speed for making the diagnosis, helping in interpreting results, and encouraging opportunistic diagnosis. With an overall rating of 4.29 on a 5-point scale, 27/36 (75%) of patients considered that the system was very useful in providing a warning about possible cases of COPD. The overall assessment of the system was 4.53 on a 5-point Likert scale with agreement to extend its use to all primary care centers. Conclusions: The proposed system provides a functional method to overcome the weaknesses detected in the current diagnostic process for COPD, which can help foster early diagnosis, while improving the diagnostic accuracy and quality of care provided.

Laparoscopy is an invasive surgical technique performed in abdominal surgery that provides faster recovery than conventional open surgeries. It requires to introduce a camera to observe the surgical maneuvers. However, during this intervention, the quality of the image may be reduced due to the creation of water vapor and carbon dioxide inside the pelvic-abdominal cavity. This phenomenon produces a nebulous image that causes interruptions during the surgical intervention. Removing this nebulous effect is a key factor to improve the vision of the surgeon. In this study, we have used a method based on the dark channel prior to remove the haze in video frames of laparoscopic surgeries to provide better quality images. The results have been positively evaluated by specialists using real video frames of laparoscopic surgeries, thus demonstrating that this method can be effective in improving the quality of the images without losing any detail of the original image.

El rango de competencias asociadas al área de sistemas distribuidos es muy amplio, extendiéndose desde el ámbito de redes de computadores hasta la administración de sistemas operativos. Este último ámbito, la administración de sistemas operativos, aparece como materia obligatoria en los planes de estudios de Ingeniería Informática pero ni siquiera se oferta como materia optativa en otras ingenierías de carácter técnico como ingeniería multimedia, ingeniería en sonido e imagen en telecomunicación o tecnologías de la información para la salud. En este proyecto se plantea analizar los conocimientos en materia de administración de sistemas a nivel de usuario de alumnos que cursan ingenierías de rama tecnológica, concretamente del grado en Tecnologías de la Información para la Salud. Para realizar este análisis se utilizará una encuesta con preguntas relacionadas con la administración de sistemas operativos; la encuesta tendrá respuestas de texto libre para no condicionar al alumnado en sus respuestas. También se pretende que la encuesta sea completada por alumnado de diferentes cursos para cubrir mejor el grado estudiado. Como resultado del proyecto se propondrá incidir en aquellos aspectos en los que el alumnado presente mayores deficiencias.

Modern neuroscience imaging technologies considerably affect diagnostic and prognostic accuracy and facilitate progress towards the cure of brain diseases. The benefits largely depend on the practicalities by which the large-scale imaging and clinical data can be integrated, examined and understood. In EU neuGRID4You (N4U) project, many datasets were generated from research centres and hospitals. In order to perform effective analyses, these datasets and their metadata along with a number of pre-computed parameters are stored in a big data repository. This paper focuses on the patient identification using big data and Fuzzy Logic, which has been achieved through fuzzy processing where a reference number called Alzheimers Disease Identification Number (ADIN) is calculated. It has enabled patients sorting for a particular intensity of Alzheimers disease, short-term estimation of the progression of that disease and context of individual patients with respect to other patients such as appropriate treatment, estimated life expectancy etc. The generated rules define the necessary knowledge base for the inference engine to generate output sets and an aggregate membership function of each rule is formed. Using this function, a most representative value of the total output set is obtained which represents the disease intensity. The implemented system and its evaluation are based on realistic datasets, demonstrators and making use of real-life neuroscience case studies. The presented results of four selected case studies show that this approach have provided sufficient expressiveness in understanding patients’ disease information. Finally, a discussion and conclusions are presented on the opportunities offered by the calculation of ADIN to manage Alzheimers disease along with potential future extensions or applications of this work.

Background and objective: Ectopic pregnancy is an important cause of morbidity and mortality worldwide. An early diagnosis, as well as the choice of the most suitable treatment for the patient is crucial to avoid possible complications. According to different factors an ectopic pregnancy must be treated from surgery, using a pharmacological treatment or following a conservative treatment. In this paper, a clinical decision support systems based on artificial intelligence algorithms has been developed to help clinicians to choose the initial treatment to be followed by the patient. Methods: A decision support system based on a three stages classifier has been developed. Each stage acts as a filter and allows re-evaluation of the classification made in the previous stage in order to find diagnostic errors. This classifier has been implemented and tested for four different aid algorithms: Multilayer Perceptron, Deep Learning, Support Vector Machine and Naives Bayes. Results: The results prove that the evaluated algorithms Support Vector Machine and Multilayer Perceptron can be useful to help gynecologists in their decisions about initial treatment, especially with Support Vector Machine that presents accuracy, sensitivity and specificity outcomes about 96.1%, 96% and 98%, respectively. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the algorithms that present a higher precision. This system would help gynecologists to take the most accurate decision about the initial treatment, thus avoiding future complications.

La promoción y protección de la salud por parte de las autoridades sanitarias tiene por objetivo fomentar estilos de vida y entornos saludables, reducir los riesgos para la salud y garantizar la atención sanitaria de calidad de todas las personas. Las tecnologías de la información y la comunicación (TIC) tienen un gran potencial para abordar algunos de los desafíos que enfrentan tanto los países desarrollados como aquellos en vías de desarrollo para proporcionar servicios de atención médica accesibles, rentables y de calidad. El trabajo de investigación de esta tesis doctoral plantea el diseño de un modelo basado en monitorización para ayudar al empoderamiento en salud. El objetivo es implementar un modelo que sea lo suficientemente general, para que se pueda adaptar fácilmente a diversas patologías y procesos de atención médica; integral, que permita la adquisición de cualquier variable de información para su monitorización; y flexible, para que pueda ser utilizado en diferentes entornos. Para instanciar este modelo, se ha seguido el paradigma de arquitecturas distribuidas orientadas a servicios (SOA) que proporciona características de interoperabilidad, robustez, independencia, concurrencia, escalabilidad, reusabilidad y tolerancia a fallos. Los resultados obtenidos a lo largo de esta investigación confirman la hipótesis planteada demostrando que los sistemas desarrollados cumplen con las funcionalidades descritas en el modelo y en la arquitectura y han contribuido al desarrollo de los modelos de monitorización enfocados al empoderamiento de los pacientes.

El área de administración de sistemas aparece como materia obligatoria en muchos de los planes de estudios del grado de Ingeniería en Informática; en cambio, en otras ingenierías afines no se oferta ni siquiera como asignatura optativa. A pesar de esto, en muchas de estas ingenierías la administración de sistemas se demanda en diferentes asignaturas e incluso se considera que los estudiantes deben saberla o aprenderla de forma autodidacta. En este trabajo se plantea estudiar el nivel de conocimientos de administración de sistemas que presentan los estudiantes en titulaciones afines a informática; posteriormente se plantea analizar los conocimientos del alumnado con las necesidades que presenten diferentes asignaturas en el plan de estudios. Finalmente, como resultado del trabajo, se propone un conjunto de actividades formativas o modificaciones en planes de estudio que conduzcan a que los egresados alcancen las competencias necesarias en administración de sistemas para cumplir con los objetivos de las asignaturas y los grados analizados., The area of systems administration appears as a compulsory subject in many of the curricula of the degree in Computer Engineering; on the other hand, in other related engineering degrees it is not offered even as an optional subject. Despite this, in many of these engineering degrees, operating systems management is demanded in different subjects and it is even considered that students should know-learn it or learn it in a self-taught way. In this work, it is proposed to study the level of knowledge of operating systems administration that the students present in computer-related degrees; later, it is proposed to analyze the knowledge of the students with the needs that present different subjects in the curriculum. Finally, as a result of the work, a set of training activities or changes in curricula are proposed that lead graduates to achieve the necessary competencies in system administration to meet the objectives.

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death according to the World Health Organization (WHO). Like any chronic disease, the clinical process of COPD affects the patient’s life. Currently, clinical processes are inefficient, causing a loss of quality of life for patients and a high economic increase in the costs borne by family members, health systems and society. This paper presents a new approach to the redesign of the process for the management of COPD based on the use of the strategy of continuous improvement Business Process Management. This approach aims to improve the efficiency of the management of chronic diseases such as COPD, while achieving a higher quality of life and patient satisfaction.

Nowadays, in many countries, stress is becoming a problem that increasingly affects the health of people. Suffering stress continuously can lead to serious behavioral disorders such as anxiety or depression. Every person, in his daily routine, can face many factors which can contribute to increase his stress level. This paper describes a flexible and distributed model to monitor environmental variables associated with stress, which provides adaptability to any environment in an agile way. This model was designed to transform stress environmental variables in value added information (key stress indicator) and to provide it to external systems, in both proactive and reactive mode. Thus, this value-added information will assist organizations and users in a personalized way helping in the detection and prevention of acute stress cases. Our proposed model is supported by an architecture that achieves the features above mentioned, in addition to interoperability, robustness, scalability, autonomy, efficient, low cost and consumption, and information availability in real time. Finally, a prototype of the system was implemented, allowing the validation of the proposal in different environments at the University of Alicante.

Monitoring chronic patients represent one of the main challenges in healthcare and information technologies. Knowledge-based and decision support systems seek to facilitate the adherence of physicians to treatments and guidelines, while improving self-management and empowerment of patients. In this work, a knowledge-based clinical decision support system is proposed for monitoring chronic patients, whose knowledge is given beforehand by human experts in the domain application.

Crohn’s disease is a chronic pathology belonging to the group of inflammatory bowel diseases. Patients suffering from Crohn’s disease must be supervised by a medical specialist for the rest of their lives; furthermore, each patient has its own characteristics and is affected by the disease in a different way, so health recommendations and treatments cannot be generalized and should be individualized for a specific patient. To achieve this personalization in a cost-effective way using technology, we propose a model based on different information flows: control, personalization, and monitoring. As a result of the model and to perform a functional validation, an architecture based on services and a prototype of the system has been defined. In this prototype, a set of different devices and technologies to monitor variables from patients and their environment has been integrated. Artificial intelligence algorithms are also included to reduce the workload related to the review and analysis of the information gathered. Due to the continuous and automated monitoring of the Crohn’s patient, this proposal can help in the personalization of the Crohn’s disease clinical process.

La enfermedad de Crohn (EC), de origen desconocido, se engloba dentro de las enfermedades inflamatorias intestinales (EII). De naturaleza crónica, es causa de una importante pérdida de calidad de vida por parte de aquellos que la sufren. Actualmente, numerosas ineficiencias concurren en el proceso clínico tanto en la fase de diagnóstico como en la de tratamiento. Este artículo presenta un nuevo enfoque basado en la aplicación sobre el proceso clínico actual de una estrategia de gestión de procesos de negocio BPM (Business Process Management) y en la implementación de mejoras que persiguen un aumento del empoderamiento del paciente.

La hipertensión es una enfermedad crónica y una causa evitable en infartos de miocardio y diferentes enfermedades cardíacas. Además, una enfermedad crónica conlleva a una reducción en la calidad de vida que puede ser mejorada haciendo uso de las tecnologías de la información y las comunicaciones. Los sistemas de ayuda a la decisión clínica tratan de mejorar la calidad de vida de un paciente crónico proporcionándole la posibilidad de auto-gestionar y tomar decisiones en todo lo relativo a su estado de salud. Sin embargo, la interacción del paciente con las nuevas tecnologías supone a menudo una dificultad. En este trabajo se propone un algoritmo genético adaptativo para personalizar de forma automática el control de la hipertensión. Para ello, se utilizan los datos de la tensión arterial del paciente en la fase del reemplazamiento generacional en computación evolutiva.