A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area.

This paper presents a scheduling problem of using multiple synthetic aperture radar (SAR) satellites to observe a large irregular area (SMA). SMA is usually considered as a kind of nonlinear combinatorial optimized problem and its solution space strongly coupled with geometry grows exponentially with the increasing magnitude of SMA. It is assumed that each solution of SMA yields a profit associated with the acquired portion of the target area, and the objective of this paper is to find the optimal solution yielding the maximal profit. The SMA is solved by means of a new method composed of three successive phases, namely, grid space construction, candidate strip generation and strip selection. First, the grid space construction is proposed to discretize the irregular area into a set of points in a specific plane rectangular coordinate system and calculate the total profit of a solution of SMA. Then, the candidate strip generation is designed to produce numerous candidate strips based on the grid space of the first phase. At last, in the strip selection, the optimal schedule for all the SAR satellites is developed based on the result of the candidate strip generation. In addition, this paper proposes a normalized grid space construction algorithm, a candidate strip generation algorithm and a tabu search algorithm with variable neighborhoods for the three successive phases, respectively. To verify the effectiveness of the proposed method in this paper, we perform simulation experiments on several scenarios and compare our method with the other seven methods. Compared to the best of the other seven methods, our proposed method can improve profit by 6.38% using the same resources.

Semi-Quantitative Discrimination of Honey Adulterated with Cane Sugar Solution by an ETongue.

This study successfully applied a potentiometric E-tongue with 20 cross-selectivity lipidic polymeric membranes in the discrimination of three semi-quantitative groups, that represented the following intervals of honey adulteration percentage with cane sugar: 0 %; [0, 10]%; [10, 20]% of adulteration. We analysed five different types of Portuguese honey; five brands of cane sugar were added to the adulterated samples; a comparative analysis was then performed. Linear discriminant analysis coupled with a tabu search algorithm for feature selection was applied to the ETongue's analytical data to select the best model. A discriminant model with 12 sensors was obtained. This model classified correctly all samples in both in internal (train data, 15 samples) and external validation (test data,10 samples). Also, multiple linear regression with tabu search was applied to verify if ETongue's data would allow quantifying the honey's adulteration level. The results showed that it was possible to obtain a quantitative model but with unsatisfactory predictive performance in the test data group (external validation), giving, in general, values below the expected concentrations. E-tongue is a real-time green, flexible and low-cost analytical tool that requires minimum sample preparation and no special technical skills, being a promising tool for everyday application.

Waste Management with the Use of Heuristic Algorithms and Internet of Things Technology.

Studies have been performed to improve the process of waste management. They were fulfilled by changing the base of waste logistics management using a combination of intelligent algorithms and the IMPACT IoT platform instead of a human factor. The research was carried out on the example of real data with respect to waste management in a given area. The proposed solution includes a program that simulates the filling of specific waste containers located in various areas. The determined aspects are inconveniences on the routes, affecting the time of moving between the receiving points and the distances between the containers. The variability of the speed and intensity of the containers filling up over time is an additional factor taken into account. The proposed methods yielded the performance of the control of the containers' filling status in real time, which apparently results in the possibility of a reaction to the current demand just in time. The proposed solution enables the improvement of the waste logistics management process, including avoiding the too-frequent emptying of containers or overfilling them. The combination of the device prototype, the simulation program, and the developed algorithms opens the possibility for further research in the smart city and optimization areas.

Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem.

The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers' expectations and shipper considerations as goals, and a common goal such as transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the genetic algorithm with the local search algorithm to solve the proposed problem. We evaluated the effectiveness of the proposed algorithm with the Tabu Search algorithm and the original genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

Conservation lessons from taboos and trolley problems.

Governments pass conservation laws, adopt policies, and make plans yet frequently fail to implement them. Implementation of conservation, however, often requires costly sacrifice: people foregoing benefit for the benefit of biodiversity. Decisions involve trade-offs with outcomes that depend on the values at stake and people's perceptions of those values. Psychology, ethics, and behavioral science have each addressed the challenge of making difficult, often tragic, trade-off decisions. Based on these literatures, values can be classified as secular or sacred, where sacred values are those for which compensation may be unthinkable (e.g., freedom). Taboo trade-offs emerge when secular values are pitted against sacred ones. These are difficult to discuss, much less negotiate. Confronting taboo trade-offs in conservation may require discursive approaches to better understand particular attributes of decisions that place sacred human values at risk. Tragic trade-offs emerge when sacred values are pitted against one another. The trolley problem-a forced choice between 2 unthinkable outcomes-is a simple heuristic illustrating ethical challenges of tragic trade-offs. Behavior studies illustrate that people have a strong aversion to losses where an active choice was made, resulting in a bias toward status quo decisions. Faced with tragic, trolley-problem-like choices, people tend to avoid taking responsibility for action, defer decisions, evade opinions on painful choices, and regret unfortunate outcomes of actions. To help close the implementation gap, conservation actors may need to directly address the psychological, ethical, and behavioral barriers created by the remorse, regret, and moral residue of implementing conservation choices that have tragic outcomes. Recognition of these predictable features of the human psyche may foster better administrative structures to support action with durable outcomes as well as new research directions.

Lecciones sobre Conservación a Partir de los Tabúes y el Dilema del Tranvía Resumen Los gobiernos aprueban leyes, adoptan políticas y elaboran planes para la conservación, pero con frecuencia fallan en implementar todo lo anterior. La implementación de la conservación, sin embargo, requiere con frecuencia un sacrificio muy costoso: que la gente renuncie a un beneficio para beneficiar a la biodiversidad. Las decisiones involucran compensaciones con resultados que dependen de los valores que están en juego y la percepción que tiene la gente de esos valores. La Psicología, la Ética y las Ciencias del Comportamiento han abordado el reto de la toma de decisiones difíciles, con frecuencia trágicas, relacionadas con las compensaciones. Con base en la literatura de estas disciplinas, podemos clasificar los valores como seculares o sagrados, donde los últimos son aquellos para los cuales una compensación puede ser inimaginable (p. ej.: la libertad). Las compensaciones tabúes emergen cuando se confrontan los valores seculares contra los sagrados y es difícil discutirlas y mucho menos negociarlas. La confrontación de las compensaciones tabú dentro del esquema de la conservación puede requerir de estrategias discursivas para entender de mejor manera los atributos particulares de las decisiones que colocan los valores humanos sagrados que están en riesgo. Las compensaciones trágicas surgen cuando se confrontan entre sí los valores sagrados. El dilema del tranvía - una elección forzada entre dos resultados impensables - es un heurístico simple que ilustra los retos éticos que representan las compensaciones trágicas. Los estudios sobre el comportamiento demuestran que las personas tienen una fuerte aversión hacia las pérdidas en las que se realizó una elección activa, lo que resultó en un sesgo hacia las decisiones del orden establecido. Cuando las personas se enfrentan a elecciones trágicas del estilo del dilema del tranvía tienden a evitar responsabilizarse por la acción, aplazar las decisiones, evadir opiniones sobre elecciones dolorosas y arrepentirse por los resultados desafortunados de las acciones. Para ayudar a reducir la brecha de la implementación, los actores de la conservación podrían necesitar abordar directamente las barreras psicológicas, éticas y de comportamiento creadas por el remordimiento, el arrepentimiento y el residuo moral de la implementación de las elecciones de conservación que tienen resultados trágicos. El reconocimiento de estos rasgos predecibles de la psique humana puede promover de mejor manera las estructuras administrativas para respaldar las acciones con resultados duraderos, así como nuevas directrices para la investigación.

Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.

BACKGROUND: This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs). METHODS: Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model. RESULTS: The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia. CONCLUSIONS: The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem.

In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with the original, hierarchical architecture. In particular, the genetic algorithm is combined with the so-called hierarchical (self-similar) iterated tabu search algorithm, which serves as a powerful local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm. The results of the conducted computational experiments demonstrate the promising performance and competitiveness of the proposed algorithm.

Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search.

This paper proposes a time difference of arrival (TDOA) passive positioning sensor selection method based on tabu search to balance the relationship between the positioning accuracy of the sensor network and system consumption. First, the passive time difference positioning model, taking into account the sensor position errors, is considered. Then, an approximate closed-form constrained total least-squares (CTLS) solution and a covariance matrix of the positioning error are provided. By introducing a Boolean selection vector, the sensor selection problem is transformed into an optimization problem that minimizes the trace of the positioning error covariance matrix. Thereafter, the tabu search method is employed to solve the transformed sensor selection problem. The simulation results show that the performance of the proposed sensor optimization method considerably approximates that of the exhaustive search method. Moreover, it can significantly reduce the running time and improve the timeliness of the algorithm.

A trilevel r-interdiction selective multi-depot vehicle routing problem with depot protection.

The determination of critical facilities in supply chain networks has been attracting the interest of the Operations Research community. Critical facilities refer to structures including bridges, railways, train/metro stations, medical facilities, roads, warehouses, and power stations among others, which are vital to the functioning of the network. In this study we address a trilevel optimization problem for the protection of depots of utmost importance in a routing network against an intelligent adversary. We formulate the problem as a defender-attacker-defender game and refer to it as the trilevel r-interdiction selective multi-depot vehicle routing problem (3LRI-SMDVRP). The defender is the decision maker in the upper level problem (ULP) who picks u depots to protect among m existing ones. In the middle level problem (MLP), the attacker destroys r depots among the (m-u) unprotected ones to bring about the biggest disruption. Finally, in the lower level problem (LLP), the decision maker is again the defender who optimizes the vehicle routes and thereby selects which customers to visit and serve in the wake of the attack. All three levels have an identical objective function which is comprised of three components. (i) Operating or acquisition cost of the vehicles. (ii) Traveling cost incurred by the vehicles. (iii) Outsourcing cost due to unvisited customers. The defender aspires to minimize this objective function while the attacker tries to maximize it. As a solution approach to this trilevel discrete optimization problem, we resort to a smart exhaustive enumeration in the ULP and MLP. For the LLP we design a metaheuristic algorithm that hybridizes Variable Neighborhood Descent and Tabu Search techniques adapted to the Selective MDVRP (SMDVRP). The performance of this algorithm is demonstrated on 33 MDVRP benchmark instances existing in the literature and 41 SMDVRP instances generated from them. Numerical experiments on a large number of 3LRI-SMDVRP instances attest that our comprehensive method is effective in dealing with the defender-attacker-defender game on multi-depot routing networks.

[Tabu search algorithm analysis on effect of Xingnaojing Injection in treatment of craniocerebral injury and complications in real world].

In this study, Tabu search algorithm was used to analyze the drug combination of Xingnaojing Injection in the treatment of brain injury and complications in the real world, and the clinical drug combination of Xingnaojing Injection in the treatment of brain injury and complications was selected and summarized. The combination of Xingnaojing Injection, Namefen and Citicoline were recommended in the treatment of brain injury and complications. For brain edema and nerve injury, Mannitol/Glycerol Fructose/Furosemide+Tetrahexose Monosialate Ganglioside Sodium should be recommended. For those with pulmonary infection, Xingnaojing Injection+Xiyanping+Ambroxol Hydrochloride+Tanreqing Injection should be recommended. For those with shock, Hydroxyethyl Starch+Dopamine Hydrochloride/Dobutamine Hydrochloride+Sodium Bicarbonate should be recommended. The combination reflected the characteristics of Chinese and Western medicine. The above medication regimen was only for clinicians' reference. The clinical application should be based on patients' specific conditions, clinical benefits and risks, as well as the incompatibility. On the basis of the findings, further studies can be carried out for prospective clinical efficacy evaluation and safety evaluation for a specific subgroup module.

[Effect of Xingnaojing Injection in treatment of cerebral hemorrhage based on Tabu search algorithm:a real world study].

In this study, Tabu search algorithm was used to analyze the effect of Xingnaojing Injection in the treatment of cerebral hemorrhage in the real world. Through the analysis of the results, the therapies based on the pathogeny of cerebral hemorrhage were screened out: Xingnaojing Injection+hemostatic drugs for promoting blood circulation and removing stasis. Cerebral hemorrhage complicated with brain edema: combined with mannitol or mannitol+aescin. The patients with relevant complications in the acute stage of cerebral hemorrhage could select according to the indications: ①Aminocycline+Oxiracetam+Piperacillin Sodium Sulbactam Sodium+Sodium Lactate Ringer; ②Aminocycline+Oxiracetam+Nifedipine+Captopril+Metoclopramide+Cimetidine; ③Insulin+Pantoprazole+So-dium Nitroprusside. The combined therapies for patients of the stable stage with complicating diseases could select according to the indications: ① Monosialotetrahexosyl Ganglioside Sodium+Deproteinized Calf Blood Serum+Nitroglycerin+Compound Potassium Dihydrogn Phosphate; ② Edaravone+Gangliosides+Captopril+Levofloxacin+Tanreqing Injection+Aminophylline. The analysis of subgroup module of drugs for promoting blood circulation and removing blood stasis suggested that the safety of traditional Chinese medicine should be paid attention to in the treatment of cerebral hemorrhage. This study was based on the data of the real world, but with some problems, such as lack of data and confounding factors. The summarized medication plan is only for the reference of clinicians. The clinical application shall be based on the specific situation of patients and the clinical benefits and risks, and pay attention to the incompatibility.

Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.

BACKGROUND: Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm. RESULTS: We compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets. CONCLUSIONS: The presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

An Improved Normalized Mutual Information Variable Selection Algorithm for Neural Network-Based Soft Sensors.

In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for soft sensors. NMIFS is applied to select influential variables contributing to the output variable and avoids selecting redundant variables by calculating mutual information (MI). A TS based strategy is designed to prevent NMIFS from falling into a local optimal solution. The proposed algorithm performs the variable selection by combining the entropy information and MI and validating error information of artificial neural networks (ANNs); therefore, it has advantages over previous MI-based variable selection algorithms. Several simulation datasets with different scales, correlations and noise parameters are implemented to demonstrate the performance of the proposed algorithm. A set of actual production data from a power plant is also used to check the performance of these algorithms. The experiments showed that the developed variable selection algorithm presents better model accuracy with fewer selected variables, compared with other state-of-the-art methods. The application of this algorithm to soft sensors can achieve reliable results.

Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response.

Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan.

A stochastic tabu search algorithm to align physician schedule with patient flow.

In this study, we consider the pretreatment phase for cancer patients. This is defined as the period between the referral to a cancer center and the confirmation of the treatment plan. Physicians have been identified as bottlenecks in this process, and the goal is to determine a weekly cyclic schedule that improves the patient flow and shortens the pretreatment duration. High uncertainty is associated with the arrival day, profile and type of cancer of each patient. We also include physician satisfaction in the objective function. We present a MIP model for the problem and develop a tabu search algorithm, considering both deterministic and stochastic cases. Experiments show that our method compares very well to CPLEX under deterministic conditions. We describe the stochastic approach in detail and present a real application.

A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption.

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.

Small-scale managed marine areas over time: Developments and challenges in a local Fijian reef fishery.

This paper investigates the Locally Managed Marine Area (LMMA) approach through looking at developments and challenges of community-based marine resource management over time, with a particular focus on Fiji in the South Pacific region. A diachronic perspective, based on two multi-method empirical studies, is used to exemplify the social complexities of the implementation of this LMMA approach in a specific island setting. This perspective connects local stakeholders' establishment and management of a LMMA covering their entire customary fishing rights area (iqoliqoli) with the national context articulated around the Fiji Locally Managed Marine Area (FLMMA) network, as well as with regional networking and international conservation dynamics. It especially explores the impacts of a small-scale marine closure (so-called tabu area) on the harvesting patterns in a portion of this LMMA, related aspects of formal and informal enforcement, and villagers' views of the health of their reef fishery. This case study reveals a lack of consensus on the current management of this closure as a conditionally-opened no-take area, whose temporary openings (re)produce social tensions, as well as a lack of consensus on the effects of this closure on the reef fishery, which is subject to poaching. The paper highlights that the articulation between conservation and extraction of marine resources, as well as between short-term and longer-term objectives of the community-based marine resource management in place, is a complex sociopolitical process even at the most local level. The discussion also points out that local observations and interpretations of coastal resource dynamics, and of the interplay between fishery and community changes, might be instrumental in addressing the limits of the area-based system of management inherent in the LMMA approach. These insights into both the development process of the LMMA approach and the challenges of its local implementation and maintenance efforts can be useful to consider the adjustments necessary for Fiji's achievement of its national coastal fisheries management strategy and its international ocean governance commitments.

Computer-aided Molecular Design of Water Compatible Visible Light Photosensitizers for Dental Adhesive.

Dental adhesive resin undergoes phase separation during its infiltration through the wet demineralized dentin and it has been observed previously that the hydrophilic-rich phase is a vulnerable region for failure due to the lack of photo-polymerization and crosslinking density. The lack of photo-polymerization is mostly due to the partitioning of photo-initiators in low concentrations within this phase. Here, a computational approach has been employed to design candidate water compatible visible light photosensitizers which could improve the photo-polymerization of the hydrophilic-rich phase. This study is an extension of our previous work. QSPRs were developed for properties related to the photo-polymerization reaction of the adhesive monomers and hydrophilicity of the photosensitizer using connectivity indices as descriptors. QSPRs and structural constraints were formulated into an optimization problem which was solved stochastically via Tabu Search. Four candidate photosensitizer molecules have been proposed here which have the iminium ion as a common feature.

A Novel Framework for Medical Web Information Foraging Using Hybrid ACO and Tabu Search.

We present in this paper a novel approach based on multi-agent technology for Web information foraging. We proposed for this purpose an architecture in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The system was implemented using a colony of artificial ants hybridized with tabu search in order to achieve more effectiveness and efficiency. To validate our proposal, experiments were conducted on MedlinePlus, a real website dedicated for research in the domain of Health in contrast to other previous works where experiments were performed on web logs datasets. The main results are promising either for those related to strong Web regularities and for the response time, which is very short and hence complies the real time constraint.

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes.

The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In particular, in our approach we simulate the patient health state and complications through discretized variables. Two types of models are presented, one entirely learned from the data and the other partially driven by literature derived knowledge. The whole cohort is simulated for fifteen years, and the simulation error (i.e. for each variable, the percentage of patients predicted in the wrong state) is calculated every year on independent test data. For each variable, the population predicted in the wrong state is below 10% on both models over time. Furthermore, the distributions of real vs. simulated patients greatly overlap. Thus, the proposed models are viable tools to support decision making in type 1 diabetes.