Enhanced Decision Support Research Articles

Supporting Decision Making in Engineering Design Using Parallel Coordinates in Virtual Reality

Computational engineering design methods and tools are common practices in the modern industry. Such approaches are integral in enabling designers to efficiently explore large and complex design spaces. However, they also tend to dramatically increase the number of candidate solutions that decision makers must correctly interpret. Because all candidate solutions can be represented in a digital form together with their assessment criteria, a natural way to explore and understand the complexities of the design problem is to visualize their multidimensional nature. The task now involves the discovery of patterns and trends within a multidimensional design space. This work aims to enhance the design decision-making process with immersive Parallel Coordinates Plot (IPCP) in virtual reality. This paper presents the design of this system, which allows representation and exploration of multidimensional scientific datasets. A qualitative validation with two surrogate expert users demonstrated that the system can be used successfully to detect both known and previously unknown patterns and support learning the decision-making process in a shorter time. The results serve as a promising indication of how immersive parallel coordinate plots can enhance decision support in complex engineering design processes.

Diagnostic Informatics-The Role of Digital Health in Diagnostic Stewardship and the Achievement of Excellence, Safety, and Value.

Diagnostic investigations (pathology laboratory and medical imaging) aim to: increase certainty of the presence or absence of disease by supporting the process of differential diagnosis; support clinical management; and monitor a patient's trajectory (e. g., disease progression or response to treatment). Digital health can be defined as the collection, storage, retrieval, transmission, and utilization of data, information, and knowledge to support healthcare. Digital health has become an essential component of the diagnostic process, helping to facilitate the accuracy and timeliness of information transfer and enhance the effectiveness of decision-making processes. Digital health is also important to diagnostic stewardship, which involves coordinated guidance and interventions to ensure the appropriate utilization of diagnostic tests for therapeutic decision-making. Diagnostic stewardship and informatics are thus important in efforts to establish shared decision-making. This is because they contribute to the establishment of shared information platforms (enabling patients to read, comment on, and share in decisions about their care) based on timely and meaningful communication. This paper will outline key diagnostic informatics and stewardship initiatives across three interrelated fields: (1) diagnostic error and the establishment of outcomes-based diagnostic research; (2) the safety and effectiveness of test result management and follow-up; and (3) digitally enhanced decision support systems.

An Animated Functional Data Analysis Interface to Cluster Rapid Lung Function Decline and Enhance Center-Level Care in Cystic Fibrosis.

Identifying disease progression through enhanced decision support tools is key to chronic management in cystic fibrosis at both the patient and care center level. Rapid decline in lung function relative to patient level and center norms is an important predictor of outcomes. Our objectives were to construct and utilize center-level classification of rapid decliners to develop an animated dashboard for comparisons within patients over time, multiple patients within centers, or between centers. A functional data analysis technique known as functional principal components analysis was applied to lung function trajectories from 18,387 patients across 247 accredited centers followed through the United States Cystic Fibrosis Foundation Patient Registry, in order to cluster patients into rapid decline phenotypes. Smaller centers (<30 patients) had older patients with lower baseline lung function and less severe rates of decline and had maximal decline later, compared to medium (30–150 patients) or large (>150 patients) centers. Small centers also had the lowest prevalence of early rapid decliners (17.7%, versus 24% and 25.7% for medium and large centers, resp.). The animated functional data analysis dashboard illustrated clustering and center-specific summaries of the rapid decline phenotypes. Clinical scenarios and utility of the center-level functional principal components analysis (FPCA) approach are considered and discussed.

Knowledge-Based System for Crop Pests and Diseases Recognition

With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

Application and evaluation of knowledge graph embeddings in biomedical data.

Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, “knowledge graphs”. The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.

Development of IoT—enabled data analytics enhance decision support system for lean manufacturing process improvement

For over three decades, production firms have extensively espoused lean manufacturing (LM) approach for constantly enhancing their operations. Of late, due to the fusion of physical and digital systems within the Industry 4.0 evolution, production systems can upgrade by applying both notions and lift operational excellence to a new high. This is primarily the reason why digital business transformation has gained significance. Moreover, Industry 4.0 that is led by data assures huge strides in output. The sheer volume of pertinent data from the production systems employing servers, sensors, and cloud computing have made the data exchange procedure more gigantic and intricate. However, conventional systems do not extensively support LM in the context of Industry 4.0. Moreover, the previous studies by researchers in the same field, shown that there was no standard platform to manage the new technologies in LM. This study presents a discussion on the interrelated framework about the way Industry 4.0 has transformed production into an industry focusing on connective mechanisms and platforms which utilize data analytics from the real world. The theoretical framework proposed in this paper integrates LM, data analytics, and Internet of Things (IoT) to enhance decision support systems in process improvement. Data analytics in simulation is employed through Internet of Things to improve bottleneck problems by maintaining the principle of LM. The main information flow route within LM decision support system is demonstrated in detail to show how the decision-making process is done. The decision support mechanism has undergone up-gradation and the suggested framework has shown that the assimilated components could function together to augment the output.

Forest Roads and Operational Wildfire Response Planning

Supporting wildfire management activities is frequently identified as a benefit of forest roads. As such, there is a growing body of research into forest road planning, construction, and maintenance to improve fire surveillance, prevention, access, and control operations. Of interest here is how road networks directly support fire control operations, and how managers incorporate that information into pre-season assessment and planning. In this communication we briefly review and illustrate how forest roads relate to recent advances in operationally focused wildfire decision support. We focus on two interrelated products used on the National Forest System and adjacent lands throughout the western USA: potential wildland fire operational delineations (PODs) and potential control locations (PCLs). We use real-world examples from the Arapaho-Roosevelt National Forest in Colorado, USA to contextualize these concepts and illustrate how fire analytics and local fire managers both identified roads as primary control features. Specifically, distance to road was identified as the most important predictor variable in the PCL boosted regression model, and 82% of manager-identified POD boundaries aligned with roads. Lastly, we discuss recommendations for future research, emphasizing roles for enhanced decision support and empirical analysis.

EXTENDING TRANSIT WINDOWS AND VESSEL DRAFTS IN PORT BOTANY USING A NEXT GENERATION, PHYSICS-BASED OPERATIONAL SYSTEM

Servicing the largest population centre in Australia, Port Botany is vital to the economic wellbeing of Sydney and New South Wales. The channel entrance is often subject to energetic Pacific Ocean swell, moderate tides and occasionally severe winds. In August 2019, the Port Authority of NSW (PANSW) adopted the NCOS ONLINE system to provide enhanced decision support for under keel clearance management of deep drafted vessels in Port Botany. The technical framework and real-life application of the physics-based operational system NCOS ONLINE is presented in this paper.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/uPvX_0DNjRo

Peak cubes in service operations: Bringing multidimensionality into decision support systems

Companies like Ritz Carlton, Disney and Verizon are among many who have invested in analytics to improve their customers' service experiences with the firms. Extensive data are collected on all aspects of how customers interact or experience the products or services. Research has shown the importance of the “peak-end” rule in service design; that is, providing a customer with good “peak” service levels and “ending” the service experience with high quality can enhance customer satisfaction and build loyalty. However, previous studies have examined this phenomenon only in contexts with unidimensional service levels. We introduce peak cubes, which enable service designers and scholars to pinpoint prominent service levels in multidimensional service experience profiles—thereby extending current research on behavioral economics and service design to more general settings. Results indicate the potential of multidimensional peak-end models to better predict customer satisfaction in various service scenarios. Using Shapley values in coalitional game theory, the resulting models can also inform service designers about the quality dimensions that are critical from the perspective of multidimensional peak-end heuristic and customer satisfaction. Our research contributions and proposed methodology will enhance decision support systems with multidimensional capabilities and have applications to fields as diverse as service operations and healthcare.

Enhanced Decision Support System for Automated Fish Feeder and Water Quality Detection with SMS Notification

Enhanced Decision Support System for Automated Fish Feeder and Water Quality Detection with SMS Notification

Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Medical decision support systems have been a core of intense research for years. The ongoing study shows that artificial intelligence has been accustomed to probe risk factors for hypertension. Factors, like health-damaging personal behaviors and changes in lifestyle and environment, are major contributors to chronic diseases. The goal of this research was to forecast the risk of developing hypertension by revealing hidden patterns in medical datasets. Quality of the data is the key to enhance the performance of learning model. But most healthcare data suffer from class imbalance problem, which induce the need for an intelligent model which can learn from such grimy data. This paper incorporates a novel approach by combing learning model and rule-based mining to offer decision support. Typically, the proposed work comprises two main implications. First suggests an intelligent learning model using boosting-based support vector machine to diagnose and expose multi-class categories in the imbalanced datasets. Finally, the enhanced predictive model is built upon the classification solution which will portray the innate data similarities. An intelligent fuzzy-based approach was employed to recognize frequent behavioral patterns. Based on these rules, valid decisions could be made to prevent hypertension. The suggested enhanced model is evaluated using a real-time hypertension dataset obtained through primary health centers. With the combination of ensemble strategies, the proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model. Thus, the efficient integration of personalized behavior with health data could provide a better understanding regarding patient health. In future this can serve as an eye toward personalized medicine.

Four Paradoxes of the User–Provider Interface: A Responsible Innovation Framework for Sea Ice Services

In the Arctic region, sea ice retreat as a decadal-scale crisis is creating a challenging environment for navigating long-term sustainability. Innovations in sea ice services can help marine users to anticipate sea ice concentration, thickness and motion, plan ahead, as well as increase the safety and sustainability of marine operations. Increasingly however, policy makers and information service providers confront paradoxical decision-making contexts in which contradictory solutions are needed to manage uncertainties across different spatial and temporal scales. This article proposes a forward-looking sea ice services framework that acknowledges four paradoxes pressuring sea ice service provision: the paradoxes of performing, contradictory functions embedded in sea ice services, contradicting desired futures and the paradox of responsible innovation. We draw on the results from a multi-year co-production process of (sub)seasonal sea ice services structured around scoping interviews, workshops and a participatory scenario process with representatives of marine sectors, fishers, hunters, metservice providers, and policy experts. Our proposed framework identifies institutionalized coproduction processes, enhanced decision support, paradoxical thinking and dimensions of responsible innovation as tactics necessary to address existing tensions in sea ice services. We highlight the role of socio-economic scenarios in implementing these tactics in support of responsible innovation in sea ice social–ecological systems. The article concludes with a discussion of questions around equity and responsibility raised by the ultimate confirmation that enhanced information, data infrastructures, and service provisions will not benefit all actors equally.

Advanced Connection Manager for Interworking between 3GPP and Non-3GPP Networks

Smart phones and handheld devices equip with multiple radio access technologies capabilities e.g. cellular and Wi-Fi now a days. It is observed that seamless session and service continuity is a difficult task while device is roaming across different radio access technologies. Connection Manager, ANDSF and Traffic Steering Algorithms are mainly used to address such switching requirements. Radio access selection decision is usually based upon various parameters like network capabilities, availability, quality, subscription and policies. This research compares network quality of all available networks for the duration and then connection manager’s decision support system ranks all suitable networks to select most appropriate access network. This paper investigates about new input parameters to be included in connection manager’s enhanced decision support system for the smarter decision of access network selection.

The Impact of Cloud Computing on Decision Support System in Adopting Knowledge Management in Saudi Organizations

Background/Objectives: Due to enormous increase in the volume of the information within and outside of the organizations, traditional business and information models are unable to cater to the needs of the modern business needs. Methods/Findings: In this context, Cloud Computing (CC) is gaining rapid popularity amongst businesses due to its ability to handle technical and organisational facets. Cloud Computing has evolved as a reliable technology, offering enhanced Decision Support Systems (DSS) at a low cost for the organizations. CC has displayed its capability of handling large volumes of data and providing seamless services to the companies. This research explored the impact of CC on DSS and Knowledge Management (KM) on Saudi organizations. The major achievement of this research was to fill the gap in knowledge by researching the impact of integration of CC, DSS and KM for Saudi organizations. The key finding of this research is that there is a significant positive impact of CC on DSS and KM in Saudi organizations. Based on this extensive research, we strongly believe that by taking recommended precautionary measures, security concerns about CC could be overcome resulting in benefits outweighing the risks for Saudi organizations. Application: Based on the literature and qualitative research, we recommend that Saudi organizations should consider migrating from traditional business models to Cloud Computing to stay competitive in this globalised world. Keywords: Cloud Computing, Decision Support Systems, Knowledge Management

How Family Caregivers Assist with Upstream Healthcare Decision-Making by Community-Dwelling Persons with Advanced Cancer: A Qualitative Study (S821)

1.Describe the roles of family caregivers in patients' healthcare decision-making in the context of advanced cancer.2.Describe two implications for outpatient and community-based early oncology palliative care concerning enhancing decision support for family caregivers. In the palliative care context, the family caregiver role in patients’ healthcare decision-making has focused on being a surrogate decision-maker at end-of-life. Less is known about family caregiver’s role in supporting upstream patient decision-making in advanced cancer. Describe how family members assist community-dwelling relatives with advanced cancer with current and prospective healthcare decisions. Qualitative descriptive study consisting of one-on-one, semi-structured interviews with persons with metastatic cancer and their family caregivers. We elicited family members’ perspectives on how they assist their relatives with any current and prospective healthcare decisions. Transcribed interviews were analyzed using a thematic analysis approach. Co-investigators reviewed and refined themes. Caregivers (n=20) averaged 56 years of age and were mostly female (95%), White (85%), and the patient’s partner/spouse (70%). Patients (n=18) averaged 58 years of age and were mostly male (67%) in “fair” or “poor” health (50%) with genitourinary (33%), lung (17%), and hematologic (17%) cancers. Themes describing family member roles in supporting patients’ decision-making were: 1) seeking information about the cancer, its trajectory, and different treatments options; 2) identifying treatment and disease decision points, including decisions about seeking emergent care; 3) ensuring family members have a common understanding of the patient’s plan of care; 4) initiating and facilitating conversations with patients about coping, values, beliefs, and “what if” scenarios about current and potential future health states and treatments; 5) implementing choices (e.g., providing transportation) and addressing “spillover” decisions (e.g., work arrangements) resulting from medical treatment choices; and 6) making upstream healthcare decisions on behalf of patients who preferred to have decisions made by their family caregivers. These data highlight a previously unreported and understudied set of critical decision partnering roles that cancer family caregivers play in patient healthcare decision-making.

OP142 Reviewing Methods For Early Assessment

IntroductionThe project DigiHelse aims to support the municipality health in Norway by offering a digital communication platform to users of the home care service nationally. In a concept stage of innovation, an early assessment of the potential socioeconomic value of the project was carried out by means of stakeholder insight and scenario drafting. As the assessment showed favorable potential in providing decision support and reducing risk, the project received funding to move into the pilot phase. The objective of this study is to reassess the effect of stakeholder insight and scenario drafting by validating the results using empirical data from the first pilot of DigiHelse.MethodsThrough collecting empirical data on resource consumption and inquiries to the service from four intervention districts and one control district in Oslo, the socioeconomic value of DigiHelse was reassessed. In addition to survey and register data collected before and after the pilot, behavioral data was introduced as a new data source.ResultsThe effect of early assessment by means of stakeholder insight and scenario drafting was successfully studied adding empirical data from the projects first pilot. The real-time data on user behavior registered in the DigiHelse server contributed to verify the assumptions from the first assessment of the project. Although the results from the analysis were less optimistic than the first assessment, the study revealed important improvement measures necessary to improve the innovation process.ConclusionsThe usefulness of early assessment is questioned, due to lack of precision of estimates caused by scarce available data. The present study presents a first step in evaluating the precision of employing stakeholder insight and scenario drafting as additional information in early assessment of innovation. The studied approach to early assessment showed potential in enhancing decision support and reducing risk from a concept stage of innovation.

An enhanced decision support approach for learning and tracking derivative index

Tracking the movement of an index involves the parameter learning from data and algorithm design for solving the decision model. In this paper, we present a factor induced robust index tracking model to protect against the parameter estimation error and immunize both systematic and default risks of tracking portfolios. A Lagrangian-based algorithm is applied to approximate optimal solutions and enhance the capacity of the decision model. Two types of inequalities are derived to strengthen the Lagrangian lower bound and speed up the whole Lagrangian Relaxation (LR) method. With the designed system, we investigate large Credit Default Swap (CDS) dataset that includes 1246 daily observations across near 500 individual contracts. We show that the fluctuation range of portfolio out-of-sample returns can be shrunk significantly by using the proposed robust counterpart, e.g. from [−12%, 12%] to [−4%, 4%] in the second half of 2013, and other comparison metrics such as Sharpe ratio and tracking error to transaction costs (TE/TC) ratio could also be consistently improved.

Enhanced Decision Support System Applications Based on Complex Intuitionistic Fuzzy Soft Expert Sets Theory

Decision making is one of the important milestones in the development of various real-life aspects. Decision Support System (DSS) is a computerized information system used to enhance decision-making in several areas. This study aims to improve DSS applications by extending two concepts of complex intuitionistic fuzzy sets and soft expert sets to propose the theory of complex intuitionistic fuzzy soft expert sets and defines some related concepts pertaining to this notion as well as the basic operations on this concept. Moreover, the algorithm for such concept is developed.

Stated Pain Levels, Opioid Prescription Volume, and Chronic Opioid Use Among United States Army Soldiers.

The use of opioids has increased drastically over the past few years and decades. As a result, concerns have mounted over serious outcomes associated with chronic opioid use (COU), including dependency and death. A greater understanding of the factors that are associated with COU will be critical if prescribers are to navigate potentially competing objectives to provide compassionate care, while reducing the overall opioid use problem. In this study, we study pain levels and opioid prescription volumes and their effects on the risk of COU.This study leveraged passive data sources that support automated decision support systems (DSSs) currently employed in a large military population. The models presented compute monthly, person-specific, adjusted probability of subsequent COT and could potentially provide critical decision support for clinicians engaged in pain management. The study population included all outpatient presentations at military medical facilities worldwide among active duty United States Army soldiers during July 2011 to September 2014 (17,664,006 encounters; population N = 552,193). We conducted a retrospective cohort study of this population and employed longitudinal data and a discrete time multivariable logistic regression model to compute COT probability scores. The contribution of pain scores and opioid prescription quantities to the probability of COT represented analytic foci. There were 13,891 subjects (2.5%) who experienced incident COT during the observed time period. Statistically significant interactions between pain scores and prescription quantity were present, in addition to effects of multiple other control variables. Counts of monthly opioid prescriptions and maximum stated pain scores per month were each positively associated with COT. A wide range in individual COT risk scores was evident. The effect of prescription volume on the COT risk was larger than the effect of the pain score, and the combined effect of larger pain scores and increased prescription quantity was moderated by the interaction term. The results verified that passive data on the US Army can support a robust COT risk computation in this population. The individual, adjusted risk level requires statistical analyses to be fully understood. Because the same data sources drive current military DSSs, this work provides the potential basis for new, evidence-based decision support resources for military clinicians. The strong, independent impact of increasing opioid prescription counts on the COT risk reinforces the importance of exploring alternatives to opioids in pain management planning. It suggests that changing provider behavior through enhanced decision support could help reduce COT rates.

Application of Bayesian statistics to seismic probabilistic safety assessment for research reactor

Nuclear facilities are susceptible to the damage due to earthquake hazards. The recent strong earthquake events show the need to explore scenarios in which the expected seismic hazard exceeds a design basis earthquake. In this regard, the seismic probabilistic safety assessment (SPSA) methodology has been developed and utilized to access the overall risk to a nuclear power plant. However, it remains challenging to deal with various uncertainties, accurately to describe correlated events, to accommodate newly observed data and to consider severe accident scenarios within the current framework. In order to overcome such challenges and take advantage of the merits of recent systems analysis concepts, this paper explores a SPSA approach by integrating the current SPSA framework with a Bayesian network and Bayesian inference instead of utilizing the standard fault tree-based technique. The proposed approach enables one to account for what are known as Aleatory and Epistemic uncertainties, to consider the correlated events, to incorporate the additional data and to conduct vulnerability assessments in an accident condition. The proposed Bayesian-based method is demonstrated by its application to a research reactor as an example. Several case studies are conducted to demonstrate how additional information such as correlated events and newly observed data changes the system-level fragility and risk. In addition, a critical scenario is investigated in a situation in which an accident has occurred for a vulnerability assessment beyond a design-basis event. Consequently, it is shown that the proposed approach provides an enhanced framework for risk assessments at nuclear facilities under earthquake hazards. This framework is ultimately expected to be extended to effective plans to mitigate system-level risk and to enhance decision support for risk-informed designs.