pretrained word-embeddings as inputs to deep classifiers. In [3], blood transfusion in the sensitive case of orthopedic procedures is scrutinized in advance to surgery, and different classifiers (such as logistic regression and decision trees) are used to open the way to this new application. In [4], daily questionnaires from mobile applications are shown to suffer from missing data, and they are completed with a principled approach based on long-term-short-term memory networks, with representative case applications given by their use for activity, food, and biometrics in athletes. In [5], adverse drug reactions are analyzed from medical records of patients with Stevens-Johnson Syndrome, using a generalized sequential pattern algorithm for early detection of their onset. In this work, the results provided with clinical information relevant for different medication groups, which can be useful for the involved physicians to stop the administration of suspected drugs in the early stages, are mined. Medical unstructured data: A different application field can be established in the unstructured data (broadly speaking, free text, images, and others) that can be obtained from Hospital Information Systems and other related sources. In [6], concept detection in medical images is addressed with the help of unsupervised learning applied to the biomedical literature, with the use of bags of visual words, autoencoders, and generative adversarial networks. In [7], the behavioral characteristics of smartphone addiction are scrutinized by automatically collecting and analyzing data through an app. Cognitive bias is present between the self-reports and the automatically collected data, and here again data mining allows to show the relevant terms and items, hence contributing to knowledge discovery in these sources. In [8], a file system structure with distributed topography is developed to support the storage of large amounts of data from wellness recordings in the Internet of Things. In [9], the integration of atomic association rules with classification is improved to adapt large amounts of data, and it is used with advantage in Tibetan medical syndrome, a model of which is built in shorter time, with fewer rules but more understandable, and with higher accuracy than the conventional methods. Medical signals: Medical signal processing is a key application set in the medical field, as many diagnostics and medical examinations are based on time-changing measured magnitudes. The electromiogram signals represent the basis for many biomedical and clinical tests, as well as for rehabilitation and prosthetic applications. In [10], stacked and sparse autoencoders are used for hand motion classification in terms of electromiogram signals in robotic arms and prosthetic hands. In [11], surface electromiogram signals are used to recognize the gait and to control the lower-limb exoskeleton or prostheses, as well as to analyze with detail the effect of load variation, using for this back propagation neural nets, support vector machines, and nearest neighbor classifiers. Another emerging field is related with biomechanics, in which a variety of waveforms naturally encode non-trivial information about our health and wellness. In [12], the proper postural habit in childhood is supported by a monitoring system classifying their sitting posture, and convolutional neural networks therein exhibit improved performance with respect to other machine learning schemes. In [13], motional patterns of human postures are identified by introducing the so-called data density functional method, by mapping the sensed time signals into specific physical spaces, and then turning them into discrete states that are detected using clustering algorithms. In [14], a detailed review puts together the topics of biomechanical feedback, motor skills, and wearables, both for sports and arts scenarios.