Prototype Decision Support System Research Articles

Assessment of Decision Support Systems for Envisioned Human Extravehicular Activity Operations: From Requirements to Validation and Verification

This work presents the results of a laboratory-based study of prototype decision support systems (DSS) for envisioned human extravehicular activity (EVA). A central feature of this work is demonstrating the explicit linkages between DSS design requirements derived from work domain demands with the validation and verification process to examine the utility of specific DSS design solutions. Two DSS prototypes were developed— Baseline and Advanced—that addressed the same set of requirements derived through a cognitive systems engineering (CSE) definition process. The Baseline design was constructed as a minimum derivation from present-day technological standards while the Advanced design incorporated novel software solutions that currently do not exist in the EVA work domain. A representative future domain of human EVA operations was constructed and utilized to evaluate the DSS designs. Both DSS prototype designs were verified to satisfy their design requirements. Furthermore, each design was validated in favor of the Advanced DSS, which outperformed the Baseless DSS in nearly all measures of performance. This work illustrates how the same set of requirements can be satisfied in multiple ways to realize effective DSS solutions.

A Prototype Decision Support System for Sustainability Performance Measurement in Furniture Industry

In measuring sustainability performance of a manufacturing system, a manager needs a practical tool to assess relevant sustainability indicators. This research aims to develop a decision support system (DSS) to measure, evaluate and improve the existing manufacturing operations toward sustainability goals. The DSS incorporates a manufacturing sustainability model that measures current manufacturing performance in Triple Bottom Line aspects comprehensively. The system provides a single index with workable indicators encompassed economic, environmental, and social dimensions. These indicators generated specifically for furniture industry in Indonesia. The DSS prototype, called EvaSus, designed as an interactive tool that helps manager to get comprehensive evaluation of the manufacturing sustainability, thus providing a way to improve the business processes.

An open access medical knowledge base for community driven diagnostic decision support system development

IntroductionWhile early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases.MethodsWe constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material.ResultsThe knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as “best suggestion” in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems.ConclusionWe built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.

A prototype decision support system for green roof type selection: A cybernetic fuzzy ANP method

The decision to install a particular type of green roof (GR) should be made during the design stage of a project due to the different costs and benefits each GR type offers. Although much research has been conducted into the various types of GR, there is a gap in our understanding of the process of decision-making during the selection of a GR type. This paper aims to develop a decision support system (DSS) for selecting the optimum type of GR for residential buildings in Kuala Lumpur. The decision criteria were initially determined using fuzzy Delphi method, followed by the creation of a database based on expert opinions, where the experts met certain criteria regarding their expertise in this field. For data analysis, a cybernetic fuzzy analytic network process was employed as a hybrid method that ignores the least important criteria in selecting the optimum type of GR for a building. Three examples are provided to illustrate the applicability of this prototype DSS in detail. Finally, it was concluded that the prototype DSS automates the entire process of GR type selection. Moreover, it enables decision-makers to select the most suitable alternative considering all significant financial and non-financial decision criteria.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Prototype of Decision Support System for the Prioritization of Maintenance Work in Medical Equipments

Los departamentos de ingeniería clínica en los hospitales son responsables de determinar y establecer un programa de mantenimiento para la administración de equipos médicos que requiera múltiples puntos de vista para avalar que los equipos médicos sean seguros y confiables. Para atenuar el riesgo a fallas funcionales, los equipos importantes y críticos deben ser identificados y priorizados. En este artículo, presentamos un modelo de toma de decisiones de criterios múltiples para priorizar los equipos médicos según su criticidad. En este documento se desarrolla un modelo de equipos médicos priorizados basado en el esquema de análisis de decisiones. La propuesta emplea redes neuronales recursivas en forma de mapas cognitivos difusos. Finalmente, se emplea un estudio de caso en el "Dr. Abel Gilbert Pontón". El hospital se presenta para mostrar la aplicabilidad del modelo propuesto y el modelo se valida con la técnica IADOV, mostrando un alto nivel de satisfacción.

The Design of a Cloud-based Clinical Decision Support System Prototype

The authors developed a mobile cloud-based clinical decision support system for drug poisoning in children. The system has a Client/Server architecture and provides a mobile application and a web service to be deployed on the Amazon Cloud infrastructure. Physicians benefit from a user interface to input patient data and to receive diagnosis and treatment protocol. The objective of this article is to help doctors, and particularly the beginners to manage and to treat children suffering from drug poisoning when toxicity is known or unknown. To do this, an intelligent system is developed. It is composed of an expert system, used when the toxic drug is known, and a case-based reasoning system applied when the toxic being ingested is unknown

Integrating wireless sensing and decision support technologies for real-time farmland monitoring and support for effective decision making

This paper presents the design of a (i) soil sensing and monitoring wireless sensor network (S2M-WSN) that can sense and monitor farmland with distributed and networked motes equipped with multiple soil sensors and (ii) a prototype decision support system (DSS) that analyses the soil data received from S2M-WSN using statistical methods, stored rules and knowledge base (KB) and auto-transmits alerts on the mobile phones of end-users. Both S2M-WSN and the DSS are integrated through (iii) predefined data frame format and interface protocol. The developed system has been deployed both in the laboratory and real crop conditions for verification, validation and performance evaluation. The system is evaluated by considering various performance metrics such as ambiguities associated with data, data packet delivery rates, support delivery performance ratio, system response time, and resultant plant growth. The developed system empowers the end users with assistance in on-field decision making through relevant advisories and alerts at right time on their mobile phones. The authors believe that the developed system would definitely bridge the information gap between end-users and domain experts for sustainable growth of agriculture.

Abstract LB-025: A decision support system for the treatment of esophageal cancer

Abstract Introduction In oncology deciding which treatment to use and when to apply them in an optimal way can be challenging. Currently there are no global approaches available to optimise the treatment path for an individual patient. Focusing on esophageal cancer, we have developed a prototype of a model-based Decision Support System (DSS). It integrates a novel range of drug, tumour and patient data to better plan clinical treatment, optimise the patient care path, and ultimately deliver improved cancer care. Methods This tool was developed using data from an historical cohort of patients with operable esophageal cancer. This cohort comprised 465 patients who were treated at Oxford University Hospitals NHS Foundation Trust between 2007 and 2014. An initial data analysis focussed on using un-supervised learning methods to identify key variables that differentiated groups of patients. These variables were then used to analyse their correlation to survival and key decision points using parametric statistical models. The final models were then used to create a web-based DSS prototype. The prototype allows users to see throughout a patient's treatment journey what factors influence prognosis including key interventions such as surgery. Results The key results within the analysis centered around treatment effects on FDG-avid nodal status, using positron emission tomography-computed tomography (PET-CT). We found that change in nodal avidity due to chemotherapy is a key prognostic factor. Furthermore, this change correlated with the change in avidity and size of the primary tumour i.e. there is a degree of correlation between the primary tumour and nodes. No difference was found between the four chemotherapy regimens analysed in their ability to reduce tumour size and also change nodal status. However a hypothesis that might be tested through further research is that titrating duration of treatment and doses to the achievement of change in avid nodal status may have a significant impact on individual patient prognosis. The above key results as well as others pertaining to attempted and successful surgery can all be explored using the dynamic web-based interface developed. Conclusion We have created a DSS tool using historical data to support Cancer Multidisciplinary Teams in understanding how key variables affect treatment choices and how these in turn relate to disease outcomes. The tool has the potential to be used to support the optimisation of treatment for individual patients based on the characteristics of their disease. Thus the DSS tool developed is a first step to personalised treatment of cancer. Citation Format: Hitesh Mistry, Fernando Ortega, Frances A Brightman, John M Findlay, Mark R Middleton, Christophe Chassagnole. A decision support system for the treatment of esophageal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-025.

Design of prototype decision support system for flood detection based on ultrasonic sensor

The system of awareness of the overflow of river water that causes flooding cannot work automatically and in real time in providing a warning about the elevation of river surface that potentially flood. It's caused residents who live around the river area do not know the situation when the river overflow. There are several alternatives to detect river overflow, one of them is by using microcontroller ultrasonic sensor based. The research aims to design prototype decision support system based on the ultrasonic sensor for flood detection that works automatically detects the river overflow. The software development model used is Prototype Model. The result of this research is a prototype in the form of microcontroller ultrasonic sensor based which can be used as flood detection decision support system that will know the height of river surface designed to detect certain level about flood potency with a low-level error.

Using Cloud IOT for disease prevention in precision agriculture

The application of decision support system (DSS) for potato late blight disease prevention has proven its benefit. In fact the DSS permits efficiency, minimizes cost and environment impact by estimating the exact requirement fungicide quantity to apply. This prediction using weather condition based late blight forecast model. The required weather information is collected from costly weather station or imprecise historical data. However, with the emergence of the IOT, huge number of low cost and low power sensors nodes can easily be deployed in farmlands in order to gather a precise climate data. Moreover, the collected data can be forwarded by Internet connection to the so called cloud IOT framework. In this paper we present a new prototype of late blight prevention decision support system based on sensor network and cloud IOT.

A Prototype Intelligent Decision-Support System with a Unified Planning and Learning Capabilities

This article summarizes reviews regarding the evolution of intelligent decision-support systems (IDSS). After doing an extensive literature survey, it was apparent that the theoretical foundation of IDSS has not undergone much improvement. The emergence and development of Artificial General Intelligence (AGI) provides a new theoretical perspective for constructing and developing IDSS. An AGI project, Non-Axiomatic Reasoning System (NARS), is built into the framework of a unified reasoning system, with a logic, Non-Axiomatic Logic (NAL). In this paper, we propose a formal model with unified planning, learning and uncertainty representation capabilities using NAL. We have developed a prototype decision-support system for urban fire-fighting within this framework, and obtained results demonstrating that this method can provide an effective way for intelligent decision-support systems.

Decision support for water quality management of contaminants of emerging concern

Water authorities and drinking water companies are challenged with the question if, where and how to abate contaminants of emerging concern in the urban water cycle. The most effective strategy under given conditions is often unclear to these stakeholders as it requires insight into several aspects of the contaminants such as sources, properties, and mitigation options. Furthermore the various parties in the urban water cycle are not always aware of each other's requirements and priorities. Processes to set priorities and come to agreements are lacking, hampering the articulation and implementation of possible solutions.To support decision makers with this task, a decision support system was developed to serve as a point of departure for getting the relevant stakeholders together and finding common ground. The decision support system was iteratively developed in stages. Stakeholders were interviewed and a decision support system prototype developed. Subsequently, this prototype was evaluated by the stakeholders and adjusted accordingly. The iterative process lead to a final system focused on the management of contaminants of emerging concern within the urban water cycle, from wastewater, surface water and groundwater to drinking water, that suggests mitigation methods beyond technical solutions. Possible wastewater and drinking water treatment techniques in combination with decentralised and non-technical methods were taken into account in an integrated way. The system contains background information on contaminants of emerging concern such as physical/chemical characteristics, toxicity and legislative frameworks, water cycle entrance pathways and a database with associated possible mitigation methods. Monitoring data can be uploaded to assess environmental and human health risks in a specific water system. The developed system was received with great interest by potential users, and implemented in an international water cycle network.

An implementation of inference in temporal branching time models

Issues concerning the implementation of temporal reasoning (inference) for models based on branching time logic as applied to intelligent decision support systems are considered. The focus is on the construction of a qualitative (interval) and quantitative (metric) branching time model. The inference is reduced to solving the temporal constraint satisfaction problem, and the corresponding procedures (algorithms) are proposed. An example of the practical application of the proposed techniques in a prototype of a real-time intelligent decision support system is described.

Prototype Decision Support System for Black Ice Detection and Road Closure Control

Black ice, a thin sheet of glazed ice on a road surface, is hard to spot by drivers, because it usually has the same color as the road. It is very hazardous and causes many car accidents each year. Unfortunately, the current static road warning signs (such as "Ice May Form on Bridge") may not draw enough attention from drivers. In order to provide in-time warning signals during black ice emergencies, this paper proposes a remote-controlled black-ice detection and warning system with economic black ice detection sensors, Geographic Information System (GIS) based database and a remote control server. This system also provides visualization functions to help users locate the affected sites on a digital map.

INTELLECTUALIZATION OF MONITORING VEHICLES BASED ON THE USE OF PRECEDENTS

The article deals with the intellectualization process of vehicle monitoring through the use of decision support systems for diagnostics of technical condition of vehicles on the basis of precedents. A generalized architecture of a tool program complex to seek solutions on the basis of precedents is proposed. A tool design precedents library is used in intelligent decision support system prototype solutions to meet the challenges of expert diagnosis and operative management of complex objects

Basin-scale hydrogeological, geophysical, geochemical and isotopic characterization: an essential tool for building a Decision Support System for the sustainable management of alluvial aquifer systems within the provinces of Milan and Monza-Brianza

CAP Group is a public company, supplying the municipalities within the provinces of Milan and Monza/Brianza (Northern Italy) with the integrated water service: 197 municipalities and more than 2 million users served, 887 wells, 154 wall-mounted tanks and hubs, a water supply network of over 7500 km, from which approximately 250 million cubic metres of water per year are withdrawn. The drinking water supply comes exclusively from groundwater resources, circulating in several overlapping aquifer systems. Basin-scale water resource management, as required by the European Water Framework Directive (2000/60/EC), is an extremely complex task. In view of this backdrop, CAP is currently developing a project called Infrastructural Aqueduct Plan that relies on a Decision Support System approach. The paper describes the preliminary steps concerning the design of a prototype Decision Support System aiming at the management of groundwater resources on a basin scale (Ticino and Adda rivers area). CAP Group Decision Support System is intended to be a package allowing for water resource assessment, identification of boundary conditions, climatic driving forces and demographic pressures, simulation and investigation of future forecasts and comparison of alternative policy measures. The project has been designed in steps including Geodatabase building, geographic information system (GIS) analysis (including multilayer analysis) and numerical modelling. The data collected in the geodatabase were analyzed to design GIS quantitative and qualitative thematic maps in order to perform the multilayer analysis of current and future state and impacts, for providing the decision maker with a comprehensive picture of the water system. The multilayer analysis relies on specific indicators based on some quantitative and qualitative data: hydrogeological, chemical, isotopic, soil use and hazards, climatic and demographic. Each parameter belonging to these macro areas were classified by 7-criticality classes scale and weights were assigned to each of them. For each macro area a synthetic index was calculated by multiplying class values with weights. These synthetic indexes were managed with a multilayer approach and compared with other models and tools (e.g. geological model, numerical groundwater model, distribution network model) in order to obtain criticality indexes. The assessment of these criticality indexes allow to evaluate alternative and strategic solutions to achieve a more efficient and sustainable water system management using a best choice approach. Currently the project team is working on multilayer analysis. The next task will be the implementation of groundwater numerical model.

Group Decision Support for Leisure Activities Using Voting and Social Networks

A group of people is often required to reach a joint decision and choose a single activity in which they will all participate. Members of such group often interact via online social networks. Group decision making requires knowledge of members’ preferences; however, in many cases the members’ preferences are not fully available. We consider a scenario where preferences are known only partially and present a group decision support framework that determines a single winning item using a voting procedure while minimizing the number of queries for members’ preferences. The framework uses a probabilistic algorithm based on the social similarity among the group members. To evaluate our framework we have built LetsDoIt, a decision support system prototype for leisure actives. We compared several types of groups and prediction methods and reveal that for groups with high internal social similarity, the algorithm reaches a decision using fewer queries, and thus less communication with the users is required. Moreover, the runtime of the algorithms using social based prediction methods is less than half that of the algorithm using rating based prediction methods. These results suggest that incorporating social similarity data, when available, can be of value.

Decision Support System Prototype to Strengthening the Organization for Children Malnutrition

Decision Support System Prototype to Strengthening the Organization for Children Malnutrition

A web-based multicriteria evaluation of spatial trade-offs between environmental and economic implications from hydraulic fracturing in a shale gas region in Ohio

Planning of shale gas infrastructure and drilling sites for hydraulic fracturing has important spatial implications. The evaluation of conflicting and competing objectives requires an explicit consideration of multiple criteria as they have important environmental and economic implications. This study presents a web-based multicriteria spatial decision support system (SDSS) prototype with a flexible and user-friendly interface that could provide educational or decision-making capabilities with respect to hydraulic fracturing site selection in eastern Ohio. One of the main features of this SDSS is to emphasize potential trade-offs between important factors of environmental and economic ramifications from hydraulic fracturing activities using a weighted linear combination (WLC) method. In the prototype, the GIS-enabled analytical components allow spontaneous visualization of available alternatives on maps which provide value-added features for decision support processes and derivation of final decision maps. The SDSS prototype also facilitates nonexpert participation capabilities using a mapping module, decision-making tool, group decision module, and social media sharing tools. The logical flow of successively presented forms and standardized criteria maps is used to generate visualization of trade-off scenarios and alternative solutions tailored to individual user's preferences that are graphed for subsequent decision-making.