Building a Risk Model for the Patientcentred Care of Multiple Chronic Diseases With the increase of multimorbidity due to population ageing, managing multiple chronic health conditions is a rising challenge. Machinelearning can contribute to a better understanding of persons with multimorbidity (PwMs) and how to design an effective framework of care and support for them. We present a risk model of older PwMs that was derived from the TILDA dataset, a longitudinal study of the ageing Irish population. This model is based on a 26nodes Bayesian network that represents patients possibly having one or more chronic conditions among diabetes, chronic obstructive pulmonary disease and arthritis, through a joint probability distribution of demographic, symptomatic and behavioral dimensions. We describe our method, give an exploratory analysis of the risk model, and assess its prediction accuracy in a crossvalidation experiment. Finally we discuss its use in supporting management of care for PwMs, drawing on comments from health practitioners on the model.

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.