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Physicians use electronic health records to monitor patient health and make more accurate prognoses, diagnoses and clinical decisions. However, with the ever increasing amounts of information stored for each patient, manually processing and digesting all this information becomes increasingly challenging, which opens an opportunity for developing clinical decision support systems such as patient trajectory modelling solutions. Patient trajectory modelling is a topic of growing research interest due to its potential to help improving health care quality by fostering preventive medicine practices, since an earlier disease diagnosis can enable better disease management and earlier intervention, along with an improved resource allocation. In this paper, we review recent approaches for patient trajectory prediction, performing a comparison based on three key aspects: 1) what is the core of the developed approach, 2) what type of data is used in the work, and 3) how is temporal information handled in the proposed solution. The resulting selection of works herein presented illustrates the current paradigm in patient trajectory modelling, and provides an overview on some of the existing challenges in this field.

Cost-effective and easy-to-manufacture antennas are key factors for 5G wireless high data rate links. In this context, we present a patch antenna designed on FR4-PCB for WiGig and backhaul applications, operating in the 60 GHz unlicensed spectrum. Yielding 20% measured 10 dB impedance-bandwidth and a maximum measured gain of 6 dBi, it stands out as a very competitive antenna compared to V -band FR4 PCB antennas found in literature. Furthermore, this antenna is suitable for illuminating a high gain dielectric lens for mobile backhaul.

Cardiovascular diseases are the leading cause of death worldwide. These diseases are related with a broad range of factors but usually show high correlation with diminished left ventricle function, which can be evaluated by measuring the ventricular ejection fraction through transthoracic echocardiography (TTE), a cost-effective and highly portable first-line diagnosing technique. Ejection fraction (EF) is currently determined through a semi-automatic process that requires manual delineation of the left ventricle area both in a diastolic and systolic frame of the patient's exam. To remove this manual annotation step, which is both time-consuming and user dependent, automatic Computer-Aided Diagnosis (CAD) systems can be used. Herein, we propose the first steps for such a system that classifies ejection fraction in four classes, based on TTE exams, with the objective of automatically providing valuable information to physicians. Our classification method is based on a 3D-Convolutional Neural Network (3D-CNN) trained on a dataset constructed with exams from a cardiology reference center. The dataset creation consisted of three main steps: firstly, for each exam, cine-loops showing the apical 4 chambers view were manually selected; then, 30 sequential frames were extracted from each cine-loop; finally, each frame was pre-processed to mask burned-in metadata. The neural network was designed to explore concepts such as convolutions using asymmetric filters and residual learning blocks. The model was trained on a dataset with 4000 TTE exams and tested on a separate dataset containing 1600 TTE cases. We obtained an accuracy of 78% and a F1 score of 71.3% for unhealthy EF (below 45%), 63.3% for intermediate EF (45-55%), 72.3% for healthy EF (55-75%) and 54.6% for abnormally high EF (above 75%). These results are promising and show that convolutional neural networks can be applied to this domain. Furthermore, this work will serve as a foundation for future research where other relevant cardiac metrics will be determined.