Data availability and algorithmic advancements provide a unique opportunity to develop a new generation of health technologies. Machine learning provides tools to tackle a wide range of questions including for example early-detection and automatic non-invasive diagnosis of disease, accurate prediction of disease progression, identification of factors underlying pathogenesis. The large-p small-n scenario is paradigmatic in biological and medical applications. Appropriate machine learning solutions must be defined taking into account this peculiarity and the heterogeneity of the data at hand, which may include molecular-omics data, patient-reported outcomes, clinical records and imaging, to name a few. Our focus is largely on neurological, oncological and immunological diseases.