Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

In this paper, we propose a model-based method for the reconstruction of not directly measured epidemiological data. To solve this task, we developed a generic optimization-based approach to compute unknown time-dependent quantities (such as states, inputs, and parameters) of discrete-time stochastic nonlinear models using a sequence of output measurements. The problem was reformulated as a stochastic nonlinear model predictive control computation, where the unknown inputs and parameters were searched as functions of the uncertain states, such that the model output followed the observations. The unknown data were approximated by Gaussian distributions. The predictive control problem was solved over a relatively long time window in three steps. First, we approximated the expected trajectories of the unknown quantities through a nonlinear deterministic problem. In the next step, we fixed the expected trajectories and computed the corresponding variances using closed-form expressions. Finally, the obtained mean and variance values were used as an initial guess to solve the stochastic problem. To reduce the estimated uncertainty of the computed states, a closed-loop input policy was considered during the optimization, where the state-dependent gain values were determined heuristically. The applicability of the approach is illustrated through the estimation of the epidemiological data of the COVID-19 pandemic in Hungary. To describe the epidemic spread, we used a slightly modified version of a previously published and validated compartmental model, in which the vaccination process was taken into account. The mean and the variance of the unknown data (e.g., the number of susceptible, infected, or recovered people) were estimated using only the daily number of hospitalized patients. The problem was reformulated as a finite-horizon predictive control problem, where the unknown time-dependent parameter, the daily transmission rate of the disease, was computed such that the expected value of the computed number of hospitalized patients fit the truly observed data as much as possible

Nonlinear model predictive control with logic constraints for COVID-19 management

The management of COVID-19 appears to be a long term chal- lenge, even in countries that have managed to suppress the epidemic after their initial outbreak. In this paper, we propose a model predictive approach for the constrained control of a nonlinear compartmental model that cap- tures the key dynamical properties of COVID-19. The control design uses the discrete-time version of the epidemic model, and it is able to handle complex, possibly time-dependent constraints, logical relations between model variables, and multiple predefined discrete levels of interventions. A state observer is also constructed for the computation of non-measured variables from the number of hospitalized patients. Five control scenarios with different cost functions and constraints are studied through numerical simulations, including an out- put feedback configuration with uncertain parameters. It is visible from the results that, depending on the cost function associated to different policy aims, the obtained controls correspond to mitigation and suppression strategies, and the constructed control inputs are similar to real life government responses. The results also clearly show the key importance of early intervention, the continuous tracking of the susceptible population and that of future work in determining the true costs of restrictive control measures and their quantitative effects

The Future of Critical Care: Innovations in Patient-Centered Technology

In the landscape of modern healthcare, the evolution of critical care has been marked by the integration of innovative technologies and the emergence of patient-centered approaches. This study aimed to explore the potential of Artificial Intelligence (AI) in shaping the future of critical care, using data collected from Centricity High Acuity data warehouse from the Anesthesia and Intensive Care Clinic and the operating theater from Emergency County Clinical Hospital "Pius Brînzeu" Timişoara. The existing healthcare landscape is characterized by the complex balance between technological advances and patient-centered care. The advent of AI presents an opportunity to revolutionize critical care, offering real-time insights and personalized interventions. This research seeks to harness the capabilities of AI to enhance patient outcomes in critical care scenarios. The study was conducted at a tertiary care hospital, using a mixed-methods approach that involved retrospective analysis of patient data from Centricity. The AI algorithms were trained on historical data to predict patient deterioration patterns, enabling timely interventions and proactive management. Results demonstrated that the integration of AI-driven insights from Centricity High Acuity data warehouse significantly improves patient outcomes. AI-assisted interventions led to reduced instances of adverse events, shorter lengths of stay, and improved resource utilization. The AI algorithms demonstrated high accuracy in predicting patient deterioration, enabling early interventions and preventing complications. In conclusion, the integration of AI technology using data from Centricity High Acuity data warehouse holds immense promise for the future of patient-centered critical care. The results indicate that AI-driven interventions can enhance patient outcomes, reduce healthcare costs, and improve resource utilization. As healthcare continues to embrace AI, the potential for transformative advancements in critical care is evident, paving the way for a new era of innovative and personalized patient-centered care

Data analysis applied to diabetic retinopathy screening: performance evaluation

The number of people with diabetes mellitus (DM) has risen from 108 million in 1980 to 422 million in 2014. Diabetic retinopathy (DR) is one of the most common causes of blindness in the developed world. A pooled analysis of data from between 1980-2008 estimates that 93 million people around the world have DR. In this paper, we present a computer-aided automated image analysis system capable of handling images generated in real-life screening program. In this study, we analyzed 2932 color fundus images taken from 733 patients with DM, of which 454 (15%) images showed signs of DR validated by human graders. The system analyzed all images by detecting anatomical components such as the optic disc, macula and vascular system of the retina, then microaneurysms (MAs) and exudates as lesions. Once the presence/absence of the structures was determined, the combination of the results was subsequently used to provide a “DR/No DR” decision using a machine learning approach. The fundus images were graded by a trained and certified expert grader as well and the final diagnosis was compared to the outcome of the computer-based approach. The performances of the MA and exudate detectors used by the system were also evaluated. The area under the ROC curve (AUC) was 0.90 with the best performing setting of the algorithm. The evaluation of the proposed approach shows that it performs well against human graders and therefore might have the potential to be used in a clinical setting. There is a need for further evaluation on large scale, real-life clinical setting to explore its clinical utility. Keywords: diabetic retinopathy, image processing, automatic screening, decision support, distributed processing MSC: 68U10, 68M1

Analysis and solution of complex route-planning problems for multi-agent systems

Planning optimal routes for Autonomuos Guided Vehicle transport systems is a crucial task for improving productivity and efficiency in several industrial applications. For such systems, there already exist models based on optimization approaches, which can compute an optimal solution, but become infeasible as the number of agents increases. In my work, a different approach for such routing environments is presented, followed by the analysis and implementation of an algorithm capable to provide suboptimal solutions for route planning problems in real time. The key concept in the investigated algorithm is avoiding problems at the time of route planning, by creating a set of routes that are conflict-, collision- and deadlock-free by design. The model, on which this work is based, was modified to support multiple type of agents - including ground and aerial vehicles - in the same environment, by the introduction of movement primitives and a three-dimensional planning graph. The algorithm was extended to handle practical problems emerging from unrealistic preconditions, and several experiments were carried out to validate my result. The experiments took place in the simulation system, created as a part of this research