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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.

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. This work has been granted by the Ministerio de Economá y Competitividad of the Spanish Government (ref. TIN2014-53067-C3-1-R) and Alberto De Ramón Fernández is supported by grant BES-2015-073611.

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.

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. This work was supported by the Ministerio de Economía y Competitividad of the Spanish Government (ref. TIN2014-53067-C3-1-R) and co-financed by FEDER.

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.

Urinary incontinence is a considerable problem which is clearly reflected in the number of patients affected by it. Moreover, it is extremely difficult to obtain an accurate diagnosis as the urinary incontinence very often is related to the neurological system. In this article a model with capabilities for urological diagnosing is proposed. This model is specialized towards the diagnosis of urological dysfunctions with neurological etiology. For this reason the model explores all the neural centres involved in both the urological phases, voiding and micturition. Once these centres have been studied it becomes possible to establish a direct relation between neural centres which present an anomalous functioning and neurological dysfunctions.

The neuronal regulator of the lower urinary tract is a very complex nervous system that consists of a heterogeneous group of neuronal centres. We have developed a new system from a model based in a multi-agent system in which each neuronal centre corresponds with an agent. This system incorporates a heuristic in order to make it more robust in the presence of possible inconsistencies. The heuristic used is based on a neural network (orthogonal associative memory). Knowledge through training has been added to the system, using correct patterns of behaviour of the urinary tract and behaviour patterns resulting from dysfunctions in two neuronal centres as a minimum. The experiments prove that the model is robust and its functioning coincides with the behaviour of the biological system. This work fulfils the expectations of providing a model of the regulator system that allows breaking the problem into simple modules each with its own entity.

In this article we present a formal framework based on the action and reaction model that allows us to cover the dynamics of multi-agent systems (MAS) made up of mobile software agents suitable for scalable networks. This model is based on the operation of the human nervous centres. At the present time, we are applying it in works related with the control of biological systems and also in those related to the network management. In the case of systems based on mobile agents, the main problem is the different vision the agents have of the world and the impossibility of being aware of and synchronizing all the influences brought by the different agents acting on it. We have compared our proposal with the conventional MAS by solving an extension of the predator-prey problem. The results show the advantages of mobility as the size of the problem grows in a distributed system.

In this article a fuzzy system with capabilities for urological diagnosing is proposed. This system is specialized towards the diagnosis of urological dysfunctions with neurological etiology. For this reason the system specifies all the neural centres involved in both the urological phases, voiding and micturition. The fuzzy system allows to classify every dysfunction of all patients by means of their membership functions. The results of the experiments show that the fuzzy approach allows the diagnosis of urological dysfunctions from the relationship between neural centres and their associated neurological dysfunction.

Currently, evolution of health services is strongly influenced by the development of information and communication technologies. Distribution of health services brings new challenges to computer systems, with regard to processing capability as well as to communications, storage and security. The proposal explained in this chapter is an architecture design to be easily adapted to advances in healthcare decentralization. It is intended to provide the capability to implement a global distribution of healthcare, even reaching the patient’s home, workplace or holiday hotel. This is a distributed architecture which is flexible to implement new functionalities and accessible from anywhere. The architecture is based on the paradigm of agents and defines the different types of agents that may form the system and their interactions.

The lower urinary tract is one of the most complex biological systems of the human body as it involved hydrodynamic properties of urine and muscle. Moreover, its complexity is increased to be managed by voluntary and involuntary neural systems. In this paper, a mathematical model of the lower urinary tract it is proposed as a preliminary study to better understand its functioning. Furthermore, another goal of that mathematical model proposal is to provide a basis for developing artificial control systems. Lower urinary tract is comprised of two interacting systems: the mechanical system and the neural regulator. The latter has the function of controlling the mechanical system to perform the voiding process. The results of the tests reproduce experimental data with high degree of accuracy. Also, these results indicate that simulations not only with healthy patients but also of patients with dysfunctions with neurological etiology present urodynamic curves very similar to those obtained in clinical studies. This study has been supported as part of the research projects: Cooperative diagnosis system applied to urological dysfunctions (GV05/158) funded by the Government of Valencia (Spain); Ambulatory wireless system for prevention of female urinary incontinence (CIT-300100-2005-7) funded by the Spanish Ministry of Science.

In this article we propose the development of a tool for helping in the medical diagnosis using neural networks, in particular the multilayer perceptron. This new tool is meant to help the urologists in obtaining an automatic diagnosis for complex multi-variable systems, and to avoid painful and costly medical treatments. The clinical study has been carried out using the medical registers of patients with dysfunctions in the lower urinary tract. The system is able to distinguish and classify dysfunctions as arreflexive, hyper-reflexive, and effort incontinence. Moreover, it is able to predict whether there is presence of dysfunction or not. The results of the experiments display a high percentage of certainty of about 85 %. The work has been supported by the Office of Science and Technology of the Autonomous Government of Valencia (Spain) as part of the research project “Cooperative diagnosis systems for Urological dysfunctions (2005-2006)”.

In this article a model of the biological neuronal regulator system of the lower urinary tract is presented. The design and the implementation of the model has been carried out using distributed artificial intelligence, more specifically a system based on agents that carry out tasks of perception, deliberation and execution. The biological regulator is formed by neuronal centres. In the model, each agent is modeled so that its behaviour is similar to that of a neuronal centre. The use of the agent paradigm in the model confers it important properties: adaptability, distributed computing, modularity, synchronous or asynchronous functioning. This strategy also allows a complex systems approach formed by connected elements whose interaction is partially well-known. We have simulated and tested the model comparing results with clinical studies.

In this paper, a model of the neuronal regulator of the lower urinary tract that regulates the micturition process is presented. A multiagent system has been used in which each agent models the behaviour of the different neuronal centres involved in the process. This model enables the distribution and emergence characteristics of neuronal regulation to be represented. Likewise, aspects such as modularity and flexibility that allow new advances in research into the nervous system to be incorporated into the model have also been taken into account. Based on the proposed model, a tool has been implemented which allows to simulate the functioning of the model showing the values related to urodymanic variables graphically. Several examples of the tests carried out with the model are presented in this paper and the results have been validated by comparing them with real data.