Information Fusion and Decision Support for Autonomous Systems

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Clinical decision support software (CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes. A literature search of articles on CDSS relevant to community pharmacy and published between 1 January 2005 and 21 October 2013 was undertaken. Articles were included if the healthcare setting was community pharmacy and the article indicated that pharmacy use of CDSS was part of the study intervention. Eight studies were found which assessed counselling, selected drug interactions, inappropriate prescribing and under-prescribing. One study was halted due to insufficient data collection. Six studies showed statistically significant improvements in the measured outcomes: increased patient counselling, 31% reduced frequency of drug-drug interactions (DDIs), reduced frequency of inappropriate medications in the elderly (2·2-1·8% patients) and in pregnant women (5·5-2·9% patients), and increased pharmacists' interventions for under-prescribed low-dose aspirin (1·74 vs. 0·91 per 100 patients with type 2 diabetes) and over-prescribed high-dose proton-pump inhibitors (PPIs) (1·67 vs. 0·17 interventions per 100 high-dose PPI prescriptions). Most studies showed improved prescribing practice, via direct communication between pharmacists and doctors or indirectly via patient education. Factors limiting the impact of improved prescribing included alert fatigue and clinical inertia. No study investigated patient outcomes and little investigation had been undertaken on how CDSS could be best implemented. Few studies have been undertaken in community pharmacy practice, and based on the positive findings reported, further research should be directed in this area, including investigation of patient outcomes.

ObjectiveThe study investigates users’ tendency to access decision support (DS) systems as a function of the correlation between the DS information and the information users already have, the ongoing interaction with such systems, and the effect of correlated information on subjective trust.BackgroundPrevious research has shown inconclusive findings regarding whether people prefer information that correlates with information they already have. Some studies conclude that individuals recognize the value of noncorrelated information, given its unique content, while others suggest that users favor correlated information as it aligns with existing evidence. The impact of the level of correlation on performance, subjective trust, and the decision to use DS remains unclear.MethodIn an experiment (N = 481), participants made classification decisions based on available information. They could also purchase additional DS with different degrees of correlation with the available information.ResultsParticipants tended to purchase information more often when the DS was not correlated with the available information. Correlated information reduced performance, and the effect of correlation on subjective trust and performance depended on DS sensitivity.ConclusionAdditional information may not improve performance when it is correlated with available information (i.e., it is redundant). Hence, the benefits of additional information and DS depend on the information the system and the operator use.ApplicationIt is essential to analyze the correlations between information sources and design the available information to allow optimal task performance and possibly minimize redundancy (e.g., by locating sensors in different positions to capture independent data).

Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

In order to solve the problem of autonomous localization and navigation caused by the loss of the position information of the mobile robot in a complex environment, a method for autonomous navigation of a mobile robot based on ROS is proposed in this paper. The ROS platform is used to avoid the problems of code redundancy, poor portability and so on. In this method, firstly, monte carlo algorithm is used to realize the autonomous localization of the robot, raster map is constructed with lidar data, and the concept of raster occupancy is introduced to reduce the influence of sensor noise. Then an improved ant colony algorithm is proposed for global path planning to realize autonomous navigation of the robot. At the end of this paper, the robot control system is combined with ROS navigation system for map construction and autonomous navigation experiments. The experimental results show that the improved ant colony algorithm can effectively shorten the number of iterations, reduce the number of iterations by 38%, improve the adaptability to the environment, facilitate the fast finding of the shortest path, and accurately realize the autonomous navigation task of the robot.

Comparative impact of guidelines, clinical data, and decision support on prescribing decisions: an interactive web experiment with simulated cases

A strange paradox haunts our attempts to prevent ischemic heart disease. Medicine has addressed the relation between serum lipids and atherosclerosis through a series of scientific triumphs. First, epidemiology demonstrated the association between abnormal cholesterol levels and coronary artery disease. Then, physiology and biochemistry elucidated the pivotal role of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase in the synthesis of lipoproteins. Next, pharmacology led to the development of new medications to target that enzyme without the often-unbearable side effects of earlier drugs. And finally, clinical researchers enrolled tens of thousands of patients at hundreds of centers worldwide to prove the efficacy of these treatments and help guide their use. Taken together, these steps represent one of the finest examples of the application of science to understand and ameliorate human disease. Article p 371 But shifting our focus from the successful laboratory or clinical trial site to the population reveals more disappointing data. We physicians fail to diagnose potentially dangerous lipid levels in up to a third of our patients with cardiovascular disease1,2; when the problem is identified, we often do not properly deploy the impressive arsenal of treatments that have been put in our hands. In the United States, fewer than half of those who would benefit from lipid-modifying treatment for coronary heart disease risk reduction are receiving it.3 And of those on therapy, just half of patients actually achieve their cholesterol goals.4 This results in part from poor persistence by physicians in monitoring lipid levels and adjusting therapy accordingly, as well as poor persistence by patients in staying with their prescribed regimens for both economic and noneconomic reasons.5 In summary, despite the wealth of tools available to diagnose and manage this common condition, only a fraction of people who have it are being adequately treated. The …

Within medical informatics there is widespread interest in computer-based decision support and the evaluation of its impact. It is widely recognized that the measurement of dependent variables, or outcomes, represents the most challenging aspect of this work. This paper describes and reports the reliability and validity of an outcome metric for studies of diagnostic decision support. The results of this study will guide the analytic methods used in our ongoing multi-site study of the effects of decision support on diagnostic reasoning. Our measurement approach conceptualizes the quality of a diagnostic hypothesis set as having two components summed to generate a composite index: a Plausibility Component derived from ratings of each hypothesis in the set, whether correct or incorrect; and a Location Component derived from the location of the correct diagnosis if it appears in the set. The reliability of this metric is determined by the extent of interrater agreement on the plausibility of diagnostic hypotheses. Validity is determined by the extent to which the index generates scores that make sense on inspection (face validity), as well as the extent to which the component scores are non-redundant and discriminate the performance of novices and experts (construct validity). Using data from the pilot and main phases of our ongoing study (n = 124 subjects working 1116 cases), the reliability of our diagnostic quality metric was found to be 0.85-0.88. The metric was found to generate, on inspection, no clearly counterintuitive scores. Using data from the pilot phase of our study (n = 12 subjects working 108 cases), the component scores were moderately correlated (r = 0.68). The composite index, computed by equally weighting both components, was found to discriminate the hypotheses of medical students and attending physicians by 0.97 standard deviation units. Based on these findings, we have adopted this metric for use in our further research exploring the impact of decision support systems on diagnostic reasoning and will make it available to the informatics research community.

The decision group is the group in which its members come together to make decisions to be implemented. In this paper, a classification of four types of decision groups by their distribution of rights and duties among group members are proposed at first. For any type of decision group, it may be possible to get the decision support from decision analysts and decision support systems that are called decision support agencies. But the required decision support may vary either with the related conditions between the decision group and support agencies or with group types. Therefore, the formal model that shows the task allocation between a decision group and decision support agencies is given for each type of decision group.

Future development in beekeeping has proved to be successful by implementing smart apiary management services that use information communication and technology (ICT) based on remote sensing tools for monitoring honybee colony health and productivity. The aim of Smart Apiculture Management Services (SAMS) is therefore, to develop an open source ICT technology that allows active monitoring and managing of bee colonies to ensures bee health and bee productivity. It also answers the requirements of beekeeping in different countries and settings, for sustainable agriculture worldwide. SAMS reduces the risks of depleting honey production that threatens livelihoods of beekeepers and degradation of pollination power of suffering bee colonies that threats overall agricultural production. It also simplify bee management while creating the possibility to understand the behaviour of bees and the environmental aspect better to ensure food production and bee farming activities. The outcome of SAMS are: a) A physical low-cost beehive model and integrated open source sensor and information transition technology, as well as energy-supply solution; b) A decision support system that combines the sensor-based data-outputs with other information sources and predictive models to measure, analyse and describe different states of the bee colony such as health, vitality and production; c) An automatic advisory support tool, which will alert the beekeeper in an easily understandable way if any aberrations from normal states are metered and will provide advice on appropriate countermeasures and d) A bee management business concept for the local production and up-scaled implementation of the developed beehives with integrated beehive monitoring system. These outcomes create jobs (particularly youths/ women), trigger investments and knowledge exchange networks.

Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-direction cells, uses the sinusoidal interference model to obtain the grid-like activity pattern, and gets optimal movement direction based on the entire set of activities. The application of adaptive resonance theory (ART) could effectively reduce resource consumption and solve the problem of stability and plasticity in the dynamic adjustment network. This brain-like system model broadens the perspective to develop more powerful autonomous robotic navigation systems. The proposed model was tested under different conditions and exhibited superior navigation performance, proving its effectiveness and reliability.

Recently, very large-scale decision support systems (DSSs) have been developed, which tackle very complex problems, associated with very extensive and polymorphic information, which probably is geographically highly dispersed. The management, updating, modification and upgrading of the data and program core of such an information system is, as a rule, a very difficult task, which encompasses many hazards and risks. The purpose of the present work was (a) to list the more significant of these hazards and risks and (b) to introduce a new general methodology for designing decision support (DS) systems that are robust and circumvent these risks. The core of this new approach was the introduction of a meta-database, called teleological, on the base of which management, updating, modification, reduction, growth and upgrading of the system may be safely and efficiently achieved. The very same teleological meta-database can be used for the construction of a sound decision support system, incorporating elements of a previous one at a future stage.

We sought to develop a computerized clinical decision support for clinicians treating patients with type 2 diabetes mellitus (T2DM). We designed, developed, and tested a computer-assisted decision support (CADS) system using statistical analyses of self-monitoring of blood glucose data, laboratory data, medical and medication history, and individualized hemoglobin A1c goals. A rule-based expert system generated recommendations for changes in therapy and accompanying explanations. A clinical decision support system (CADS) was developed that considers 9 classes of medications and 69 regimens with combinations of up to 4 therapeutic agents. The preferred sequences of regimens can be customized. The program is integrated with a "comprehensive diabetes management system," electronic medical record systems, and a method for uploading data from memory glucose meters via telephone without use of a computer or the Internet. The software provides a report to the clinician regarding the overall quality of glycemic control and identifies problems, e.g., hypoglycemia, hyperglycemia, glycemic variability, and insufficient data. The program can recommend continuation of current therapy, adjustment of dosages of current medications, or change of regimen and can provide explanations for its recommendations. If the user rejects the recommendations, the program will recommend alternative approaches. The CADS also provides access to Food and Drug Administration-approved prescribing information, guidelines from professional organizations, and selections from the general medical literature. The system has been extensively tested with real and synthetic data and is ready for evaluation in multicenter clinical trials. A clinical decision support system to assist with the management of patients with T2DM was designed, developed, tested, and found to perform well.

Renal dosing clinical decision support (CDS) systems have demonstrated clinical effectiveness and potential benefits for patient outcomes. However, the high override rates consistently reported are problematic and undesirable. To understand providers' use patterns of renal dosing CDS, we investigated the override reasons obtained from primary care practices affiliated with two teaching hospitals. We selected a stratified random sample of 300 alerts and reviewed electronic medical records. Appropriateness criteria and an inter-rater reliability process were used. We found that two thirds of alerts were overridden inappropriately, and this proportion was similar for frequent over-riders as compared to the remainder of physicians. These findings imply that strategies are needed to convince providers to accept more clinically appropriate suggestions, though they need to be broadly targeted.