How Continuous Monitoring Changes the Interaction of Patients with a Mobile Telemedicine System

The use of continuous glucose monitor changes the way patients manage their diabetes, as observed in the increased number of daily insulin bolus, the increased number of daily BG measurements, and the differences in the distribution of BG measurements throughout the day. Continuous monitoring also increases the interaction of patients with the information system and modifies their patterns of use

Electronic Report Generation Web Service evaluated within a Telemedicine System

This work presents a generic tool based on a client-server architecture that generates electronic reports helping the evaluation process of any information system. For the specific evaluation of telemedicine systems the defined reports cover four dimensions: auditory of the system; evolution of clinical protocols; results from the questionnaires for user acceptance and quality of life; and surveillance of clinical variables. The use of a Web Service approach allows multiplatform use of the developed electronic report service and the modularity followed in the implementation enables easy system evolution and scalability

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles

Parallel workflows to personalize clinical guidelines recommendations: application to gestational diabetes mellitus

The MobiGuide system provides patients with personalized decision support tools, based on computerized clinical guidelines, in a mobile environment. The generic capabilities of the system will be demonstrated applied to the clinical domain of Gestational Diabetes (GD). This paper presents a methodology to identify personalized recommendations, obtained from the analysis of the GD guideline. We added a conceptual parallel part to the formalization of the GD guideline called "parallel workflow" that allows considering patient?s personal context and preferences. As a result of analysing the GD guideline and eliciting medical knowledge, we identified three different types of personalized advices (therapy, measurements and upcoming events) that will be implemented to perform patients? guiding at home, supported by the MobiGuide system. These results will be essential to determine the distribution of functionalities between mobile and server decision support capabilities

Clasificación de medidas de glucemia en función de ingestas en diabetes gestacional

Este trabajo presenta un clasificador de medidas de glucemia en función de las ingestas asociadas para pacientes con diabetes gestacional. Se presentan los resultados obtenidos al comparar la relevancia de diferentes atributos así como del uso de dos de los algoritmos más populares en el mundo del aprendizaje automático: las redes neuronales y los árboles de decisión. El estudio se ha realizado con los datos de 53 pacientes pertenecientes al Hospital de Sabadell y al Hospital Mutua de Terrassa obteniendo un 91,72% de precisión en el caso de la red neuronal, y un 95.92% con el árbol de decisión. La clasificación automática de medidas de glucemia permitirá a los especialistas pautar un tratamiento más acertado en base a la información obtenida directamente del glucómetro de las pacientes, contribuyendo así al desarrollo de los sistemas automáticos de ayuda a la decisión para diabetes gestacional

Design evaluation of a prototype user interface to support a guideline-based decision support system in gestational diabetes

Gestational Diabetes (GD) has increased over the last 20 years, affecting up to 15% of pregnant women worldwide. The complications associated can be reduced with the appropriate glycemic control during the pregnancy

Artificial-intelligence-augmented telemedicine applied to the management of diet-treated gestational diabetes

Gestational diabetes (GD) confers an increased risk of complications as well as future type 2 diabetes. We assess the safety and efficacy of an artificial intelligence (AI)-augmented telemedicine system (ruled-based reasoning) that includes a blood glucose (BG) classifier (C4.5 Quinlan decision tree) in comparison with the standard care in the management of GD while insulin is not required

High-resolution patterns of palaeoenvironmental changes during the Little Ice Age and the Medieval Climate Anomaly in the northwestern Iberian Peninsula

ABSTRACT: A high resolution core (9.7 yr cm-1 ) from the Chao de Veiga Mol raised bog (NW Iberian Peninsula) was analyzed to identify plant macrofossils, estimate peat humification and calculate hydroclimatic indices based on current bog species, with the overall aim of determining the climate conditions associated with evolution of the bog during the Medieval Climate Anomaly and the Little Ice Age. These proxies, together with historical and climate data, proved to be good indicators of the changes in bog surface wetness. Analysis: of the core led to identification of 9 different periods: two corresponding to the so-called Medieval Climate Anomaly (930 to 1345 AD, 1075–665 calibrated years before present [cal. yr BP]); four corresponding to the Little Ice Age (1345 to 1905 AD; 665–105 cal yr BP); and three corresponding to the last century (1905 to 2000 AD). The findings revealed a generally dry climate that lasted until the 14th century, followed by a transition to a long period with a more humid, but characteristically very variable climate, which ended at the beginning of the 20th century and was followed by a rapid transition to more humid conditions and finally, a change to drier conditions. The Medieval Climate Anomaly was indicated by the abundance of dry-adapted mosses (Leucobryum glaucum, Hypnum cupressiforme) and characterized by warm dry conditions and high levels of peat humification, with alternating wet phases. The LIA period was dated by a large abundance of Sphagnum species (an indicator of wetness) and a gradual increase in the humification index. However, four different climate phases were differentiated in this period. High-resolution reconstruction of the evolution of the CVM bog and the multiproxy approach have together enabled a more detailed identification of climatic variations in this area, which are generally consistent with the global models, as well as better definition of the elusive climatic oscillations in the last millennium and confirmation of the importance of local modulation of global models. The study provides new information and a detailed chronology of climatic events that will help to refine local modulation of the climate evolution model in the still quite unexplored region of the NW Iberian Peninsula, a key area for understanding the paleoclimatic dynamics in SW Europe.This research was funded with the support of the Xunta de Galicia government (Spain) through projects INCITE09-200-019-PR and Consolidacion e Estructuracion 2018 GRC GI- 1243-GEMAP, ED431C 2018/32

Mobile Telemedicine for Diabetes Care

Diabetes Mellitus is nowadays one of the most frequent non-contagious diseases in the world and remains a major health problem for the national health care programs. It is well proved that Telemedicine helps diabetic patients controlling their glucose levels, facilitating their day to-day therapy management and the communication with health care personnel. The rapid growth and development of information technologies in the areas of mobile computing and mobile Internet is shaping a new technological scenario of telemedicine and shared care systems. In this chapter we will show one approach to Mobile Telemedicine for Diabetes Care

Artificial pancreas using a personalized rule-based controller achieves overnight normoglycemia in patients with type 1 diabetes

Objective: This study assessed the efficacy of a closed-loop (CL) system consisting of a predictive rule-based algorithm (pRBA) on achieving nocturnal and postprandial normoglycemia in patients with type 1 diabetes mellitus (T1DM). The algorithm is personalized for each patient’s data using two different strategies to control nocturnal and postprandial periods. Research Design and Methods: We performed a randomized crossover clinical study in which 10 T1DM patients treated with continuous subcutaneous insulin infusion (CSII) spent two nonconsecutive nights in the research facility: one with their usual CSII pattern (open-loop [OL]) and one controlled by the pRBA (CL). The CL period lasted from 10 p.m. to 10 a.m., including overnight control, and control of breakfast. Venous samples for blood glucose (BG) measurement were collected every 20 min. Results: Time spent in normoglycemia (BG, 3.9–8.0 mmol/L) during the nocturnal period (12 a.m.–8 a.m.), expressed as median (interquartile range), increased from 66.6% (8.3–75%) with OL to 95.8% (73–100%) using the CL algorithm (P<0.05). Median time in hypoglycemia (BG, <3.9 mmol/L) was reduced from 4.2% (0–21%) in the OL night to 0.0% (0.0–0.0%) in the CL night (P<0.05). Nine hypoglycemic events (<3.9 mmol/L) were recorded with OL compared with one using CL. The postprandial glycemic excursion was not lower when the CL system was used in comparison with conventional preprandial bolus: time in target (3.9–10.0 mmol/L) 58.3% (29.1–87.5%) versus 50.0% (50–100%). Conclusions: A highly precise personalized pRBA obtains nocturnal normoglycemia, without significant hypoglycemia, in T1DM patients. There appears to be no clear benefit of CL over prandial bolus on the postprandial glycemi