Interactive Decision Support System Research Articles

O57: Tailored risk assessment and forecasting in intermittent claudication using machine learning (TRAFIC-ML)

Abstract Introduction Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the initial treatment for patients with intermittent claudication(IC). However, implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. We propose a precise machine learning derived decision support system that aims to provide personalised outcome predictions across different management strategies. Methods Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample of 10,000 patients based on 255 patients from our vascular centre. The model considered 27 baseline characteristics, compliance to best medical therapy/smoking cessation and treatment strategy. The model was validated using a separate dataset of 254 patients. This model was then used to build a prototype interactive decision support system which was evaluated using calibration curves, decision curve analyses and area under the receiver operator characteristic (AUROC) curves. Results The AUROC curves demonstrated excellent discrimination for the risk of progression to chronic limb threatening ischaemia at two years(0.892) and five years(0.866) and likelihood of major adverse cardiovascular events(0.836), major adverse limb events(0.891) and revascularisation(0.896)within 5 years, regardless of the treatment strategy. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analyses confirmed clinical utility. The tool maintained an accuracy of >80% and an effect size of >0.5. Conclusion Our decision support system successfully predicts outcomes in IC regardless of treatment strategy, offering potential for improved risk stratification and patient outcomes.

A Regional 100 m Soil Grid-Based Geographic Decision Support System to Support the Planning of New Sustainable Vineyards

A WebGis tool called GoProsit has been developed to support winegrowers in planning a new sustainable vineyard and in the identification of high-quality terroir in Tuscany, Central Italy, by providing various information on soils, climate, hydrological risks, and fertilization. GoProsit, hosted by the web platform GEAPP, is a free, user-friendly, and interactive Geographic Decision Support System (GDSS). Soil data behind the WebGis tool has a 1 ha resolution, achieved by processing the legacy vector-type soil database of the Tuscany Region with the DSMART (Disaggregation and Harmonization of Soil Map Units Through Resampled Classification Trees as supervised classification) algorithm, which disaggregated the map to 297,023 vineyard grid cells. Each grid cell holds climatic and pedologic information, along with physical and chemical features for each horizon of the most probable soil. GoProsit also provides soil maps in image format obtained by georeferencing about 50 historical soil maps (1969–2012). Finally, GoProsit runs and returns the outputs of six models: (a) carbon footprint, (b) potential erosion and maximum vine row length compatible with tolerable erosion, (c) potential water stress, (d) risk of runoff/waterlogging, (e) identification of suitable rootstocks, and (f) nutritional needs before planting. Statistics of the main model results for the investigated area are reported. This promising tool will soon be usable for the whole Italian territory; however, its potential makes it suitable for use in any wine-growing district.

A web-based interactive decision support system for a multi-objective lot-sizing and production scheduling model

In the course of globalization and the corresponding steadily growing market dynamics, companies are faced with an increasing challenge to remain competitive. Therefore, the efficient use of the available resources, while maintaining a high level of service and customer satisfaction, is a key instrument for ensuring success. Within this framework, production scheduling can be seen as one of the most decisive operational activities of a manufacturing company, with the production orders being sequenced in a way that allows the achievement of an efficient allocation of the available resources. Furthermore, the correlated lot-sizing represents a crucial influencing factor in production scheduling. In this article, a novel methodology to solve multi-objective lot-sizing and production scheduling problems is proposed. Essentially, it provides an interactive three-phase framework that supports a procedure for simultaneous decision-making regarding lot-sizing and production scheduling. The main goal is not only to find an appropriate production schedule that best meets the decision-maker’s preferences but also to gain a deeper insight into the relationship among criteria and a better understanding of possible trade-offs between competing objectives. To guide the user through the whole decision-making process, a web-based interactive application was developed. The proposed methodology was applied to a case study regarding an automotive supplier company, revealing itself as a viable and practical technique able to solve real-world multi-objective lot-sizing and production scheduling problems in the manufacturing industry.

Repurposing a Digital Twin-Based Decision Support System as a Training Tool by Incorporating Learning Science Principles

The U.S. Navy has a sophisticated manpower planning process for recruiting, selecting, training, and placing thousands of enlisted Sailors into hundreds of specific jobs every year. Given the large number of “moving parts” in this manpower planning process, some inefficiencies are unavoidable. For example, there may be a non-trivial time gap between when some Sailors complete their job-specific training course and when they arrive at their duty station. During this time, their knowledge and skills will invariably decay due to lack of use. Electronic job aids, such as Interactive Electronic Technical Manuals (IETMs) and Decision Support Systems (DSS), are important to mission success because they help to reduce the need for Sailors to memorize rarely used factual details. However, IETMs and DSSs are job aids, not training tools. In this paper, we describe how we repurposed an existing digital twin-based DSS to create a ubiquitous, online training tool. We did so by leveraging the learning science principles of human performance measurement, adaptive learning with scaffolding, performance feedback, and training data analytics. We conclude with a series of best practices and lessons learned that other practitioners could follow when attempting to repurpose electronic job aids that were not originally designed for training.

Prediction tool for renal adaptation after living kidney donation using interpretable machine learning.

Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors' high life expectancy and elderly donors' comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning. The study included 823 living kidney donors who underwent nephrectomy in 2009-2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m2 and ≥ 65% of the pre-donation values, respectively. The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762-0.930) and 0.626 (0.541-0.712), while the areas under the precision-recall curve were 0.965 (0.944-0.978) and 0.709 (0.647-0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed. The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.

An Interactive Decision Support System for College Degree Planning

Many students in the Unites States enter college without having decided on a focus for their studies, and thus are faced with choosing from a large number of potential majors and associated very complex sets of degree requirements which can include many courses in other areas of study. Academic advisors use academic planning tools to help students make decisions about class schedules, selecting an academic major or minor, planning for graduation, and many other academic related activities. There is a dearth of decision support systems for degree planning, mainly due to the complexity of degree requirements, and thus many existing academic planning tools utilize static documents or PDF files for displaying information pertaining to degree requirements and course prerequisites. This work considers the complexity of degree requirements and presents the design and implementation of an efficient interactive decision support system that helps students explore degree completion paths. Keywords: degree planning, academic advising, academic decision making, decision support system, college degree planning

Interactive decision support system with machine intelligence for augmentative communication

Interactive decision support system with machine intelligence for augmentative communication

An Interactive Decision Support System for Land Reuse Tasks.

Experts face the task of deciding where and how land reuse-transforming previously used areas into landscape and utility areas-can be performed. This decision is based on which area should be used, which restrictions exist, and which conditions have to be fulfilled for reusing this area. Information about the restrictions and the conditions is available as mostly textual, nonspatial data associated to areas overlapping the target areas. Due to the large amount of possible combinations of restrictions and conditions overlapping (partially) the target area, this decision process becomes quite tedious and cumbersome. Moreover, it proves to be useful to identify similar regions that have reached different stages of development within the dataset which in turn allows determining common tasks for these regions. We support the experts in accomplishing these tasks by providing aggregated representations as well as multiple coordinated views together with category filters and selection mechanisms implemented in an interactive decision support system. Textual information is linked to these visualizations enabling the experts to justify their decisions. Evaluating our approach using a standard SUS questionnaire suggests that especially the experts were very satisfied with the interactive decision support system.

Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization

In many practical applications, the end-goal of multi-objective optimization is to select an implementable solution that is close to the Pareto-optimal front while satisfying the decision maker’s preferences. The decision making process is challenging since it involves the manual consideration of all solutions. The field of multi-criteria decision making offers many methods that help the decision maker in this process. However, most methods only focus on analyzing the solutions’ objective values. A more informed decision generally requires the additional knowledge of how different preferences affect the variable values. One difficulty in realizing this is that while the preferences are often expressed in the objective space, the knowledge required to implement a preferred solution exists in the decision space. In this paper, we propose a decision support system that allows interactive knowledge discovery and knowledge visualization to support practitioners by simultaneously considering preferences in the objective space and their impact in the decision space. The knowledge discovery step can use either of two recently proposed data mining techniques for extracting decision rules that conform to given preferences, while the extracted knowledge is visualized via a novel graph-based approach that allows the discovery of important variables, their values and their interactions with other variables. The result is an intuitive and interactive decision support system that aids the entire decision making process — from solution visualization to knowledge visualization. We demonstrate the usefulness of this system on benchmark optimization problems up to 10 objectives and real-world problems with up to six objectives.

Understanding Differences in Behaviour Patterns of Healthcare Service Elements Among Regions Applying Data Mining

Maternal and child healthcare (MCH) has always been regarded as one of the important issues globally. The aim of this article is to find out all frequently occurring healthcare service elements along with their availability conditions and their interactions with other health system (HS) elements, when coverage of priority MCH intervention was either poor or moderate or good in any region. Association rule mining technique has been used to understand the probability of occurrences of different healthcare service elements among regions. Along with the proposed analytical framework, an interactive decision support system (DSS) has also been developed on a web platform, which would help healthcare policymakers to integrate the analytical framework easily in their processes of decision-making. The system has been developed by using Shiny package on R software. Data for all variables are collected for 584 Indian districts from the third phase of district-level household and facility survey. From results, it is observed that community healthcare services, sub-centre (SC)-level healthcare services and PHC-level healthcare services of a region are very much interlinked with each other, and their relationships define the healthcare condition of that region.

DSSApple: A hybrid expert system for the diagnosis of post-harvest diseases of apple

Post-harvest diseases of apple can cause considerable economic losses. Thus, we developed DSSApple, an interactive web-based decision support system, that helps users to diagnose post-harvest diseases of domesticated apple based on observed macroscopic symptoms on fruit. Specifically, DSSApple is designed as a two-stream hybrid diagnostic tool, that can be effectively used by both expert and non-expert users to diagnose diseased instances of apple. The image-based stream allows the user to interact simply by selecting pictures, representing the variety of symptoms of diseases at different stages of the infection and on different cultivars. Instead, the expert-based stream of the system incrementally collects user feedback about the target disease by asking questions related to the macroscopic characteristics of the observed symptoms on a target apple. The expert-based reasoning mechanism of DSSApple is developed by leveraging the framework of Bayesian Networks (BNs). We detail the process of building this knowledge base with the support of a domain expert. We further exploit the BN to process incomplete or conflicting user feedback within the inference mechanism as well as to provide human-understandable explanations on the suggested diagnoses. The proposed hybrid approach has been thoroughly evaluated in two studies, involving simulated (by photos) as well as real infected apples. Thus, the proposed hybrid version of DSSApple is able to outperform both the single streams and the user intuition in terms of diagnostic accuracy.

Gamifying Decision Support Systems to Promote Inclusive and Engaged Urban Resilience Planning

Urban residents are often unevenly vulnerable to extreme weather and climate events due to socio-economic factors and insufficient greenspace. This can be amplified if citizens are not meaningfully consulted in the planning and design decisions, with changes to greenspace having detrimental impacts on local communities, e.g., through green gentrification. These deficiencies can be addressed through inclusive landscape-level collaborative planning and design processes, where residents are fully engaged in the co-creation of urban greenspaces. A promising way to support co-creation efforts is gamifying technology-based interactive decision support systems (DSSs). Gamification, the incorporation of video game elements or play into non-game contexts, has previously been used for DSSs in urban planning and to inform the public about the impacts of climate change. However, this has yet to combine informational goals with design-play functionality in the redesign of urban greenspaces. We conducted a review of state-of-the-art video game DSSs used for urban planning engagement and climate education. Here, we propose that gamified DSSs should incorporate educational elements about climate change alongside the interactive and engaging elements of urban planning games, particularly for real-world scenarios. This cross-disciplinary approach can facilitate improved community engagement in greenspace planning, informing design and management strategies to ensure multiple benefits for people and the environment in climate-vulnerable cities.

An interactive decision support system for real-time ambulance relocation with priority guidelines

Changes in demand patterns and unexpected events are the two primary sources of delays in healthcare emergency operations. To mitigate such delays, researchers proposed the movement of idle ambulances between emergency bases as one of the effective ways to improve the areal coverage of future demands. In this study, we have developed a model-driven decision support system that simultaneously seeks to maximize demand coverage while minimizing travel time by optimally relocating emergency response vehicles. The developed mathematical model partitions and prioritizes demand into four categories and continuously updates them over time. Furthermore, it dynamically calculates the number of coverages in different regions based on the current location of idle ambulances. Also, we developed a real-time risk assessment DSS for recommended relocations, which could be utilized as a reference by the EMS user while implementing suggested relocation decisions. A real case study is used to validate the proposed DSS, and its final output is compared to the existing operational policy. The findings show that the average workload added to each ambulance due to relocations has significantly improved the response time and coverage ratio. Compared to the existing operational policy, the developed decision support system decreased the time to respond to calls, which was deemed to be more than to offsets the increase in travel time due to relocation. Furthermore, the system also reduced the total working time of all ambulances by about 9% per shift.

LogisticsLab: An academic software for decision-making in logistics

Logistical decision problems are a part of many courses in the field of logistics, management and operations research. It makes sense to illustrate these optimisation problems using case studies, which can be reproduced by students using suitable software. Often, solver add-ins in spreadsheets programs or general optimisation software are used, which on the one hand requires a high level of knowledge in Operations Research and on the other hand does not always allow an intuitive approach. This article describes the academic software LogisticsLab with which the distributors tie in with the idea of interactive decision support systems to systematically combine the experiences and intuitions of human decision-makers with the possibilities of computer-assisted modelling and optimisation of a wide range of logistical decisions.

Maximizing student opportunities for in-person classes under pandemic capacity reductions

In this article, we describe the decision support system that was developed for the assignment of courses to teaching modalities and rooms for the Fall semester of 2020 at the University of Connecticut (UConn). With the adoption of safety/mitigation standards imposed by the COVID-19 pandemic, the seating capacities of rooms were reduced by more than 70%, thus making virtually every existing room assignment for Fall 2020 infeasible. The demand for in-person instruction required the reassignment of a large number of courses to rooms, where not all requests for physical space could be accommodated. In order to maximize opportunities for in-person instruction, UConn introduced a teaching modality in which class meetings are attended on campus by only 50% of the enrolled students. As decision makers were given partial flexibility to assign teaching modalities to classes, the complexity of the assignment problem increased considerably, especially because the real-world instances involved hundreds of rooms and thousands of classes and required a quick solution turnaround in practice. In this article, we introduce this flexible assignment problem and describe the two mixed-integer programming formulations that were used to solve the real-world instances of the problem; in particular, one of the formulations leverages structural properties presented in this work in order to represent the problem in a more compact way. We explain how we tailored our algorithms to solve the real-world problem, describe the dynamics of the interactive decision support system created in this initiative, and present insights derived from our study.

Application of the RSPARROW Modeling Tool to Estimate Total Nitrogen Sources to Streams and Evaluate Source Reduction Management Scenarios in the Grande River Basin, Brazil

Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km2). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.

A transnational cooperation for sustainable use and management of non-native trees in urban, peri-urban and forest ecosystems in the Alpine region (ALPTREES)

The expected benefits and potential risks of non-native tree species (NNT) to European geographic regions have polarized the opinions of experts and citizens. Benefits include climate change (CC) mitigation and adaptation, contributions to bioeconomy, urban and peri-urban green infrastructure and mitigation of natural hazards. However, NNT may become invasive and thus may pose risks to native biodiversity, ecosystem functioning or socio-economy. In critical and vulnerable ecosystems such as the Alpine Space (AS), such risks and benefits must be carefully considered before management decisions are made. Experiences in the management of NNT in urban areas, peri-urban, rural territories and forests are often region- or city-specific and rarely shared. Given the challenges in NNT management with respect to both benefits and risks, an European transnational approach is needed to qualify the role of NNT in future AS ecosystems. The objective is to provide a transnational strategy for a responsible use and sustainable management of NNT in the AS with the help of an integrated Decision Support System. The project fits within the context of national and regional site-derived policy aiming at protecting and enhancing biodiversity to ensure ecological connectivity and cultural resources while maintaining a high level of resilience and ecosystem services across the AS. Implementation activities of the ALPTREES project include developing a comprehensive database on NNT, projecting the current and potential distribution of NNT in the AS under CC scenarios determining their invasive potential and analyzing the different ecosystem services provided by NNT to assess the tradeoffs between risks and benefits. developing a comprehensive database on NNT, projecting the current and potential distribution of NNT in the AS under CC scenarios determining their invasive potential and analyzing the different ecosystem services provided by NNT to assess the tradeoffs between risks and benefits. ALPTREES will formulate management recommendations for NNT under different climate and economic scenarios based on experience from sample plots combined with model projections, citizen science, and multiple stakeholder meetings. With the help of policy briefs, a handbook of lessons learned, transnational pilot actions for best management practices, and an inter-active online Decision Support System a close stakeholder interaction and -benefit will be ensured Another innovative output of the project is the Open ALPTREES Knowledge Hub, that will support the transnational and interdisciplinary knowledge transfer.

A knowledge-based material selection system for interactive pressure vessel design

Continual introduction of new materials and improvements in existing materials increase the variety of materials that can be used for pressure vessel components. Among wide variety of materials, the most suitable one must be selected for a component by matching its functional requirements with various available materials’ specifications. This study proposes an interactive knowledge-based decision support system for selecting the most suitable material for a given pressure vessel component and its working environment. The developed decision support system, namely Pressure Vessel SELection (PVSEL), consists of two separate phases. In the first elimination phase of PVSEL, the user obtains a feasible set of alternative materials by answering various questions and providing lower-limit values at materials’ critical specifications. PVSEL, then, uses a ranking phase which uses ELECTRE, TOPSIS and VIKOR methods to rank the feasible materials. In the second phase, each alternative material’s ranking is determined by combining its performance values at weighted critical specifications (selection criteria), which are considered as important in meeting the functional requirements of the component. Usage of PVSEL is illustrated in the paper and the results show that the proposed PVSEL is an effective selection tool and provides meaningful results for the designers.

An interactive multi-criteria spatial decision support system for energy retrofitting of building stocks using CommuntiyVIZ to support urban energy planning

A major concern for any city is urban energy planning, which is particularly multi-sectoral and multi-actor oriented. This concern is especially critical for the built-up urban zones, which are predominantly responsible for the bulk of energy consumption and carbon emissions. Hence, an interdisciplinary, integrated approach is needed to address this complex challenge. This study focuses on both energy retrofit interventions for existing building stocks and the integration of participative processes in decision-making. It deals with the development of a stakeholder-oriented approach to implementing effective strategies in urban energy planning. This can help define meaningful building energy retrofitting scenarios that focus on energy consumption and environmental impact, in addition to economic and social considerations. A major outcome of this study is the development of a new multicriteria spatial decision support system (MC-SDSS) that is an interactive energy-related plug-in for a geographic information system (GIS) environment, adapted from CommunityViz. The methodology used to deliver the tool can be applied to other contexts and situations due to its flexibility. The new MC-SDSS facilitates the decision-making process for stakeholders who ask “what if” questions and visualise “if-then” scenarios in real time to handle the entire planning procedure adequately.

Interactive decision support system for planned student scheduling using same-time scheduling in timetable

Student scheduling takes place after the timetable is prepared. This is a very simple activity. This study is aimed at creating an interactive decision support system for planning related to student scheduling. This planning creates constraints at the pre-timetabling stage so that the complex part of student scheduling is easy at the post-timetabling stage. This study aims to address simultaneous study of backlog courses alongwith regular courses. Two methodologies are suggested to create these constraints which can be used to create different sets of courses scheduled at the same time. These methodologies employ assignment problem iteratively to arrive at courses to be scheduled at the same time. This research work has been tested with the real datasets of four semesters.