Abstract: Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.

Abstract: Purpose: A surface electronic brachytherapy (EBT) device is in fact an x-ray source collimated with specific applicators. Low-energy (<100 kVp) x-ray beam dosimetry faces several challenges that need to be addressed. A number of calibration protocols have been published for x-ray beam dosimetry. The media in which measurements are performed are the fundamental difference between them. The aim of this study was to evaluate the surface dose rate of a low-energy x-ray source with small field applicators using different calibration standards and different small-volume ionization chambers, comparing the values and uncertainties of each methodology. Methods: The surface dose rate of the EBT unit Esteya (Elekta Brachytherapy, The Netherlands), a 69.5 kVp x-ray source with applicators of 10, 15, 20, 25, and 30 mm diameter, was evaluated using the AAPM TG-61 (based on air kerma) and International Atomic Energy Agency (IAEA) TRS-398 (based on absorbed dose to water) dosimetry protocols for low-energy photon beams. A plane parallel T34013 ionization chamber (PTW Freiburg, Germany) calibrated in terms of both absorbed dose to water and air kerma was used to compare the two dosimetry protocols. Another PTW chamber of the same model was used to evaluate the reproducibility between these chambers. Measurements were also performed with two different Exradin A20 (Standard Imaging, Inc., Middleton, WI) chambers calibrated in terms of air kerma. Results: Differences between surface dose rates measured in air and in water using the T34013 chamber range from 1.6% to 3.3%. No field size dependence has been observed. Differences are below 3.7% when measurements with the A20 and the T34013 chambers calibrated in air are compared. Estimated uncertainty (with coverage factor k = 1) for the T34013 chamber calibrated in water is 2.2%-2.4%, whereas it increases to 2.5% and 2.7% for the A20 and T34013 chambers calibrated in air, respectively. The output factors, measured with the PTW chambers, differ by less than 1.1% for any applicator size when compared to the output factors that were measured with the A20 chamber. Conclusions: Measurements using both dosimetric protocols are consistent, once the overall uncertainties are considered. There is also consistency between measurements performed with both chambers calibrated in air. Both the T34013 and A20 chambers have negligible stem effect. Any x-ray surface brachytherapy system, including Esteya, can be characterized using either one of these calibration protocols and ionization chambers. Having less correction factors, lower uncertainty, and based on measurements, performed in closer to clinical conditions, the TRS-398 protocol seems to be the preferred option.

Abstract: Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.