Abstract: Visual analytics (VA) provides an interactive way to explore vast amounts of data and find interesting patterns. This has already benefited the development of computational models, as the patterns found using VA can then become essential elements of the model. Similarly, recent advances in the use of VA for the data cleaning stage are relevant to computational modelling given the importance of having reliable data to populate and check models. In this paper, we demonstrate via case studies of medical models that VA can be very valuable at the conceptual stage, to both examine the fit of a conceptual model with the underlying data and assess possible gaps in the model. The case studies were realized using different modelling tools (e.g., system dynamics or network modelling), which emphasizes that the relevance of VA to medical modelling cuts across techniques. Finally, we discuss how the interdisciplinary nature of modelling for medical applications requires an increased support for collaboration, and we suggest several areas of research to improve the intake and experience of VA for collaborative modelling in medicine.

Abstract: The emergence of Ebola in West Africa is of worldwide public health concern. Successful mitigation of epidemics requires coordinated, well-planned intervention strategies that are specific to the pathogen, transmission modality, population, and available resources. Modeling and simulation in the field of computational epidemiology provides predictions of expected outcomes that are used by public policy planners in setting response strategies. Developing up to date models of population structures, daily activities, and movement has proven challenging for developing countries due to limited governmental resources. Recent collaborations (in 2012 and 2014) with telecom providers have given public health researchers access to Big Data needed to build high-fidelity models. Researchers now have access to billions of anonymized, detailed call data records (CDR) of mobile devices for several West African countries. In addition to official census records, these CDR datasets provide insights into the actual population locations, densities, movement, travel patterns, and migration in hard to reach areas. These datasets allow for the construction of population, activity, and movement models. For the first time, these models provide computational support of health related decision making in these developing areas (via simulation-based studies). New models, datasets, and simulation software were produced to assist in mitigating the continuing outbreak of Ebola. Existing models of disease characteristics, propagation, and progression were updated for the current circulating strain of Ebola. The simulation process required the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract governmental policy level). The predictive results from this system were validated against results from the CDC's high-level predictions.

Abstract: High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. The paper describes a new algorithm for ECG signal denoising, applicable in the contest of the real-time health monitoring using mobile devices, where the signal processing efficiency is a strict requirement. The proposed algorithm is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that removes the noise’s frequencies without the implementation of the Fast Fourier Transform that would require the use of special optimized libraries. It is composed by only few code lines and hence offers the possibility of implementation on mobile computing devices in an easy way. Moreover, the scheme allows the local denoising and hence a real time visualization of the denoised signal. Experiments on real datasets have been carried out in order to test the algorithm from accuracy and computational point of view.

Abstract: Syncytial tissues, such as the smooth muscle of the urinary bladder wall, are known to produce action potentials (spikes) with marked differences in their shapes and sizes. The need for this diversity is currently unknown, and neither is their origin understood. The small size of the cells, their syncytial arrangement, and the complex nature of innervation poses significant challenges for the experimental investigation of such tissues. To obtain better insight, we present here a three-dimensional electrical model of smooth muscle syncytium, developed using the compartmental modeling technique, with each cell possessing active channel mechanisms capable of producing an action potential. This enables investigation of the syncytial effect on action potential shapes and their propagation. We show how a single spike shape could undergo modulation, resulting in diverse shapes, owing to the syncytial nature of the tissue. Difference in the action potential features could impact their capacity to propagate through a syncytium. This is illustrated through comparison of two distinct action potential mechanisms. A better understanding of the origin of the various spike shapes would have significant implications in pathology, assisting in evaluating the underlying cause and directing their treatment.

Abstract: Epilepsy is one of the most common neurological diseases, affecting about 1% of the world population, of all ages, genders, origins. About one third of the epileptic patients cannot be treated by medication or surgery: they suffer from refractory epilepsy and must live with their seizures during all their lives. A seizure can happen anytime, anywhere, imposing severe constrains in the professional and social lives of these patients. The development of transportable and comfortable devices, able to capture a sufficient number of EEG scalp channels, to digitally process the signal, to extract appropriate features from the EEG raw signals, and give these features to machine learning classifiers, is an important objective that a large research community is pursuing worldwide. The classifiers must detect the pre-ictal time (some minutes before the seizure). In this presentation the problem is presented, solutions are proposed, results are discussed. The problem is formulated as a classification of high-dimensional datasets, with unbalanced four classes. Preprocessing of raw data, classification using Artificial Neural Networks and Support Vector Machines to the 275 patients of the European Epilepsy Database show that computer science, in this case machine learning, will have an important role in the problem. For about 30% of the patients we found results with clinical relevance. Real-time experiments made with some patients, in clinical environment and at home will be shown (including video) and discussed. The problem is still challenging the computer science community researching in medical applications. New research directions will be pointed out in the presentation.