TreC platform. An integrated and evolving care model for patients empowerment and data repository. Abstract   Introduction  Health care has been deeply revolutionized by the new wave of information and communication technology (ICT) development, particularly related to the electronicpersonal health record (PHR). The present paper describes the original design and implementation approach followed in the Trentino Province (Italy) to promote an Integrated Care Model for patientsxe2x80x99 empowerment and data repository, by means of an evolving Personal Health Record  PHR platform, named TreC (Trentino Citizens Clinical Record).    Materials and Methods  The TreC Platform is conceived as a communication hub among different stakeholders. The core assumption of the TreC platform strategy is to consider the citizenpatient as main manager and owner of both hisher own health and hisher contacts with the health care systems.    Results  Over the years, the TreC platform has represented the core pillar in the digitalization process promoted at Province level. This has been strategically embedded in the multifaceted egovernment strategy endorsed by the Province of Trento. So far (October 2018), more than 89,000 citizens within the Province of Trento are using TreC platform as a way to communicate with the health care system and access their own personal health records    Conclusions  The experience gained through the TreC platform implementation and its results are promising, supporting the idea that a PHR platform can represent a key driving factor in improving health care quality and efficiency, both from a patient and a health care staff perspective.

Deep learning for electronic health records: A comparative review of multiple deep neural architectures Abstract   Despite the recent developments in deep learning models, their applications in clinical decisionsupport systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic health records (EHR) for personalised prediction of risks and health trajectories. While this can be a promising trend, vast papertopaper variability (from data sources and models they use to the clinical questions they attempt to answer) have hampered the fieldxe2x80x99s ability to simply compare and contrast such models for a given application of interest. Thus, in this paper, we aim to provide a comparative review of the key deep learning architectures that have been applied to EHR data. Furthermore, we also aim to: (1) introduce and use one of the worldxe2x80x99s largest and most complex linked primary care EHR datasets (i.e., Clinical Practice Research Datalink, or CPRD) as a new asset for training such datahungry models; (2) provide a guideline for working with EHR data for deep learning; (3) share some of the best practices for assessing the xe2x80x9cgoodnessxe2x80x9d of deeplearning models in clinical risk prediction; (4) and propose future research ideas for making deep learning models more suitable for the EHR data. Our results highlight the difficulties of working with highly imbalanced datasets, and show that sequential deep learning architectures such as RNN may be more suitable to deal with the temporal nature of EHR.