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.

The growing demand for physical rehabilitation processes can result in the rising of costs and waiting lists, becoming a threat to healthcare services' sustainability. Telerehabilitation solutions can help in this issue by discharging patients from points of care while improving their adherence to treatment. Sensing devices are used to collect data so that the physiotherapists can monitor and evaluate the patients' activity in the scheduled sessions. This paper presents a software platform that aims to meet the needs of the rehabilitation experts and the patients along a physical rehabilitation plan, allowing its use in outpatient scenarios. It is meant to be low-cost and easy-to-use, improving patients and experts experience. We show the satisfactory results already obtained from its use, in terms of the accuracy evaluating the exercises, and the degree of users' acceptance. We conclude that this platform is suitable and technically feasible to carry out rehabilitation plans outside the point of care.

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Abstract. Any effort to improve the efficiency of the physical rehabilitation processes is fundamental to ensure the sustainability of healthcare services. This efficiency depends greatly on the patient's adherence to the rehabilitation treatments. Information and communication technologies can help in these issues offering solutions that aim to monitor the patients' rehabilitation exercises performance allowing the existence of domiciliary rehabilitation scenarios. We have developed a solution of this kind, which aims to be as simple and low-cost as possible in the way of how recognizes and evaluates patient's movements. In this work we show a comparison between the use of a multilayer perceptron and a distance between patterns measuring algorithm for patients' motion recognition.