Defining an OWL ontology that could be used to integrate mental-health risk information within a decision support system

In the field of mental health risk assessment, there is no standardisation between the data used in different systems. As a first step towards the possible interchange of data between assessment tools, an ontology has been constructed for a particular one, GRiST (Galatean Risk Screening Tool). We briefly introduce GRiST and its data structures, then describe the ontology and the benefits that have already been realised from the construction process. For example, the ontology has been used to check the consistency of the various trees used in the model. We then consider potential uses in integration of data from other sources. © 2009 IEEE.

Psychological cue use and implications for a clinical decision support system

Effective clinical decision making depends upon identifying possible outcomes for a patient, selecting relevant cues, and processing the cues to arrive at accurate judgements of each outcome's probability of occurrence. These activities can be considered as classification tasks. This paper describes a new model of psychological classification that explains how people use cues to determine class or outcome likelihoods. It proposes that clinicians respond to conditional probabilities of outcomes given cues and that these probabilities compete with each other for influence on classification. The model explains why people appear to respond to base rates inappropriately, thereby overestimating the occurrence of rare categories, and a clinical example is provided for predicting suicide risk. The model makes an effective representation for expert clinical judgements and its psychological validity enables it to generate explanations in a form that is comprehensible to clinicians. It is a strong candidate for incorporation within a decision support system for mental-health risk assessment, where it can link with statistical and pattern recognition tools applied to a database of patients. The symbiotic combination of empirical evidence and clinical expertise can provide an important web-based resource for risk assessment, including multi-disciplinary education and training. © 2002 Informa UK Ltd All rights reserved.

Decision support system for pipeline route selection

Presents information on a study which proposed a decision support system (DSS) for a petroleum pipeline route selection with the application of analytical hierarchy process. Factors governing route-selection for cross-country petroleum pipelines; Application of the DSS from an Indian perspective; Cost benefit comparison of the shortest route and the optimal route; Results and findings.

Clinical Decision Support System Sonares

A decision support system SonaRes destined to guide and help the ultrasound operators is proposed and compared with the existing ones. The system is based on rules and images and can be used as a second opinion in the process of ultrasound examination.

MultiDecision-2: A Multicriteria Decision Support System

The paper presents a multicriteria decision support system, called MultiDecision-2, which consists of two independent parts - MKA-2 subsystem and MKO-2 subsystem. MultiDecision-2 software system supports the decision makers (DMs) in the solving process of different problems of multicriteria analysis and linear (continues and integer) problems of multicriteria optimization. The two subsystems MKA-2 and MKO-2 of of MultiDecision-2 are briefly described in the paper in the terms of the class of the problems being solved, the system structure, the operation with the interface modules for input data entry and the information about DM’s local preferences, as well as the operation with the interface modules for visualization of the current and final solutions.

A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments

Integrated supplier selection and order allocation is an important decision for both designing and operating supply chains. This decision is often influenced by the concerned stakeholders, suppliers, plant operators and customers in different tiers. As firms continue to seek competitive advantage through supply chain design and operations they aim to create optimized supply chains. This calls for on one hand consideration of multiple conflicting criteria and on the other hand consideration of uncertainties of demand and supply. Although there are studies on supplier selection using advanced mathematical models to cover a stochastic approach, multiple criteria decision making techniques and multiple stakeholder requirements separately, according to authors' knowledge there is no work that integrates these three aspects in a common framework. This paper proposes an integrated method for dealing with such problems using a combined Analytic Hierarchy Process-Quality Function Deployment (AHP-QFD) and chance constrained optimization algorithm approach that selects appropriate suppliers and allocates orders optimally between them. The effectiveness of the proposed decision support system has been demonstrated through application and validation in the bioenergy industry.

A Multicriteria Decision Support System MultiDecision-1

* This paper is partially supported by the National Science Fund of Bulgarian Ministry of Education and Science under contract № I–1401\2004 "Interactive Algorithms and Software Systems Supporting Multicriteria Decision Making."

A Real-time Decision Support System Prototype for Management of a Power Block

This paper describes the basic tools for a real-time decision support system of a semiotic type on the example of the prototype for management and monitoring of a nuclear power block implemented on the basis of the tool complex G2+GDA using cognitive graphics and parallel processing. This work was supported by RFBR (project 02-07-90042).

Decision support system for vendor managed inventory supply chain:a case study

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs).

Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.

A simulation-based decision support system for evaluating operating policies in an emergency room facility

Increased pressure to control costs and increased competition has prompted health care managers to look for tools to effectively operate their institutions. This research sought a framework for the development of a Simulation-Based Decision Support System (SB-DSS) to evaluate operating policies. A prototype of this SB-DSS was developed. It incorporates a simulation model that uses real or simulated data. ER decisions have been categorized and, for each one, an implementation plan has been devised. Several issues of integrating heterogeneous tools have been addressed. The prototype revealed that simulation can truly be used in this environment in a timely fashion because the simulation model has been complemented with a series of decision-making routines. These routines use a hierarchical approach to organize the various scenarios under which the model may run and to partially reconfigure the ARENA model at run time. Hence, the SB-DSS tailors its responses to each node in the hierarchy.

Modulating membership grades to gain consensus for fuzzy set uncertainty values in a clinical decision support system

This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.

A Computer-Aided Decision Support System for Making Locational Decisions, 1990

This report is the product of a first-year research project in the University Transportation Centers Program. This project was carried out by an interdisciplinary research team at The University of Iowa's Public Policy Center. The project developed a computerized system to support decisions on locating facilities that serve rural areas while minimizing transportation costs. The system integrates transportation databases with algorithms that specify efficient locations and allocate demand efficiently to service regions; the results of these algorithms are used interactively by decision makers. The authors developed documentation for the system so that others could apply it to estimate the transportation and route requirements of alternative locations and identify locations that meet certain criteria with the least cost. The system was developed and tested on two transportation-related problems in Iowa, and this report uses these applications to illustrate how the system can be used.

The effect of a Computerised Decision Support System (CDSS) on compliance with the prehospital assessment process: results of an interrupted time-series study

BACKGROUND: Errors in the decision-making process are probably the main threat to patient safety in the prehospital setting. The reason can be the change of focus in prehospital care from the traditional "scoop and run" practice to a more complex assessment and this new focus imposes real demands on clinical judgment. The use of Clinical Guidelines (CG) is a common strategy for cognitively supporting the prehospital providers. However, there are studies that suggest that the compliance with CG in some cases is low in the prehospital setting. One possible way to increase compliance with guidelines could be to introduce guidelines in a Computerized Decision Support System (CDSS). There is limited evidence relating to the effect of CDSS in a prehospital setting. The present study aimed to evaluate the effect of CDSS on compliance with the basic assessment process described in the prehospital CG and the effect of On Scene Time (OST). METHODS: In this time-series study, data from prehospital medical records were collected on a weekly basis during the study period. Medical records were rated with the guidance of a rating protocol and data on OST were collected. The difference between baseline and the intervention period was assessed by a segmented regression. RESULTS: In this study, 371 patients were included. Compliance with the assessment process described in the prehospital CG was stable during the baseline period. Following the introduction of the CDSS, compliance rose significantly. The post-intervention slope was stable. The CDSS had no significant effect on OST. CONCLUSIONS: The use of CDSS in prehospital care has the ability to increase compliance with the assessment process of patients with a medical emergency. This study was unable to demonstrate any effects of OST.