A c c e p t e d M a n u s c r i p t Highlights  We propose an alternative system for classifying the risk in paediatric congenital heart surgery. Four methods are tested: a perceptron multilayer, self-organising maps, a radial basis function neural network and decision trees. We obtain an accuracy of 99.87% (using pre and post-surgical data) and 83% (using just pre-surgical data).Page 2 In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. MethodsWe have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. ResultsAccuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. ConclusionsAccording to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

An abdominal aortic aneurysm is an abnormal dilatation of the aortic vessel at abdominal level. This disease presents high rate of mortality and complications causing a decrease in the quality of life and increasing the cost of treatment. To estimate the mortality risk of patients undergoing surgery is complex due to the variables associated. The use of clinical decision support systems based on machine learning could help medical staff to improve the results of surgery and get a better understanding of the disease. In this work, the authors present a predictive system of inhospital mortality in patients who were undergoing to open repair of abdominal aortic aneurysm. Different methods as multilayer perceptron, radial basis function and Bayesian networks are used. Results are measured in terms of accuracy, sensitivity and specificity of the classifiers, achieving an accuracy higher than 95%. The developing of a system based on the algorithms tested can be useful for medical staff in order to make a better planning of care and reducing undesirable surgery results and the cost of the post-surgical treatments.

This article tackles several problems faced by professionals in physiotherapy: the performance of the rehabilitation exercises by the patients, the control of the course of the illness and the patient's ignorance about whether or not he is properly performing the exercises. We propose a solution based on the use of the Wii Controller to control the exercise movements, along with software that provides the patient with an easy, intuitive and interactive control system. Finally, web services are used to allow the remote monitoring of the treatment by physiotherapy professionals.

Hypertension affects one in five adults worldwide. Healthcare processes require interdisciplinary cooperation and coordination between medical teams, clinical processes, and patients. The lack of patients’ empowerment and adherence to treatment makes necessary to integrate patients, data collecting devices and clinical processes. For this reason, in this paper we propose a model based on Business Process Management paradigm, together with a group of technologies, techniques and IT principles which increase the benefits of the paradigm. To achieve the proposed model, the clinical process of the hypertension is analyzed with the objective of detecting weaknesses and improving the process. Once the process is analyzed, an architecture that joins health devices and environmental sensors, together with an information system, has been developed. To test the architecture, a web system connected with health monitors and environment sensors, and with a mobile app have been implemented.

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