Making diagnosis explicit

What is good diagnostic practice? The answer is elusive for many medical students and equally puzzling for those trying to build effective medical decision support systems. Much of the problem lies in the difficult of 'getting at' diagnosis. Expert diagnosticians find it difficult to introspect on their own strategies, thus making it difficult to pass on their expertise.Traditional knowledge acquisition methods are designed for gathering static domain knowledge and are inappropriate for the acquisition of knowledge about the diagnos¬ tic 'task'. More advanced knowledge acquisition methodologies, particularly those which focus on the modelling of problem-solving knowledge seem to hold more promise, but are not sufficiently practicable to allow anyone other than a knowledge engineer to operate directly. Given the difficulty experts have in accessing their own diagnostic strategies what is needed is a tool which would enable diagnosticians themselves to directly formu¬ late and experiment with their own methods of diagnosis.This research describes the development of a knowledge acquisition methodology geared specifically towards the exposition of medical diagnosis. The methodology is implemented as a toolkit enabling exploration and construction of medical diagnostic models and production of model-based medical diagnostic support systems. The toolkit allows someone skilled in diagnosis to articulate their diagnostic strategy so that it can be used by those with less experience

Designing a graphical decision support tool to improve system acquisition decisions

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Includes bibliographical references (p. 130-133).System acquisition decision makers are frequently charged with choosing a single system from a set of feasible possibilities that could best fulfill the needs of their organizations. While numerous rules and regulations are already in place for both commercial and government acquisitions to ensure the acquisitions are conducted fairly, decision makers need greater support than rules and regulations alone can provide. The acquisition decision is a complex data analysis problem, where the decision maker must analyze multiple candidate systems on a number of performance and cost metrics. To understand this multivariate environment, decision makers must analyze the system data at multiple levels of reasoning. This research proposes a decision support tool that best supports system acquisition decision makers by providing them with graphical representations displaying how well candidate systems fulfill their organizations' needs. System acquisition decisions require support of three basic levels of reasoning (Data Processing, Information Aggregation, and Knowledge Synthesis) in order to perform system trade-offs on relevant system metrics. To test how well decision support tools could support system acquisition decision makers, two graphical decision support tools were designed: a traditional separable display and a new configural display named Fan Visualization (FanVis). To compare the effectiveness of FanVis against a traditional separable display, an experiment was conducted where participants answered a series of system acquisition questions across the three levels of reasoning.(cont.) Analysis of the experimental results indicate that FanVis and the separable displays support a system acquisition decision maker, but to different degrees across the three levels of reasoning. Comparatively, participants tended to have higher performance on Knowledge Synthesis tasks using FanVis, while they tended to have a higher performance on Data Processing tasks using the separable display. When examining subjective measures, FanVis was the preferred tool of choice. Through use of an eye tracking device, it was further determined that participants also exhibited erratic fixation patterns on those questions that were answered incorrectly compared to those answered correctly. Further, it was determined that FanVis allowed participants to maintain more efficient gaze patterns regardless of task, whereas participants used less efficient gaze patterns in the separable display for some tasks. Additionally, participants tended to spend a greater frequency of time fixating on relevant elements in FanVis while completing Knowledge Synthesis tasks, while the opposite was true for Data Processing tasks, suggesting that performance and time spent fixating on relevant information is correlated. From the results of this experiment, a set of design implications was created for future system acquisition decision support tools.by Anna Elizabeth Massie.S.M

OWL-based acquisition and editing of computer-interpretable guidelines with the CompGuide editor

Computer-Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence-based nature of their source material. Therefore, these machine-readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine-readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition.COMPETE, Grant/Award Number: POCI-01-0145-FEDER-007043; FCT - Fundacao para a Ciencia e Tecnologia, Grant/Award Number: UID/CEC/00319/201

A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale

Forests are part of a complex landscape mosaic and play a crucial role for people living both in rural and urbanized spaces. Recent progresses in modelling and Decision Support System (DSS) applied to the forestry sector promise to improve public participative forest management and decision-making in planning and conservation issues. However, most DSS are not open-source systems, being in many cases software designed for site-specific applications in forest ecosystems. Furthermore, some of these systems often miss challenging the integration of other land uses within the landscape matrix, which is a key issue in modern forestry planning aiming at linking recent developments in open-source Spatial-DSS systems to sectorial forest knowledge. This paper aims at demonstrating that a new type of S-DSS, developed within the Life+ project SOILCONSWEB over an open-source Geospatial Cyber-Infrastructure (GCI) platform, can provide a strategic web-based operational tool for forest resources management and multi-purpose planning. In order to perform simulation modelling, all accessible via the Web, the GCI platform supports acquisition and processing of both static and dynamic data (e.g., spatial distribution of soil and forest types, growing stock and yield), data visualization and computer on-the-fly applications. The DSS forestry tool has been applied to a forest area of 5,574 ha in the southern Apennines of Peninsular Italy, and it has been designed to address forest knowledge and management providing operational support to private forest owners and decision-makers involved in management of forest landscape at different levels. Such a geospatial S-DSS tool for supporting integrated forest knowledge at landscape represents a promising tool to implement sustainable forest management and planning. Results and output of the platform will be shown through a short selection of practical case studies

ToolSHeD™: The development and evaluation of a decision support tool for health and safety in construction design

Purpose - The purpose of this paper is to describe an innovative information and decision support tool (ToolSHeD(TM)) developed to help construction designers to integrate the management of OHS risk into the design process. The underlying structure of the prototype web-based system and the process of knowledge acquisition and modelling are described. Design/methodology/approach - The ToolSHeD(TM) research and development project involved the capture of expert reasoning regarding design impacts upon occupational health and safety (OHS) risk. This knowledge was structured using an innovative method well-suited to modelling knowledge in the context of uncertainty and discretionary decision-making. Example "argument trees" are presented, representing the reasoning used by a panel of experts to assess the risk of falling from height during roof maintenance work. The advantage of using this method for modelling OHS knowledge, compared to the use of simplistic rules, is discussed. Findings - The ToolSHeD™ prototype development and testing reveals that argument trees can represent design safety risk knowledge effectively. Practical implications - The translation of argument trees into a web-based decision support tool is described and the potential impact of this tool in providing construction designers (architects and engineers) with easy and inexpensive access to expert OHS knowledge is discussed. Originality/value - The paper describes a new computer application, currently undergoing testing in the Australian building and construction industry. Its originality lies in the fact that ToolSHeD(TM) deploys argument trees to represent expert OHS reasoning, overcoming inherent limitations in rule-based expert systems

Knowledge based expert system pavement management optimization

Knowledge-based expert systems and dynamic programming are used for development of a comprehensive pavement management system tool to help engineers and planners to make objective, consistent, and cost effective decisions regarding pavement maintenance, rehabilitation, and reconstruction;Knowledge-based expert system provide a flexible tool to allow for acquisition of knowledge from experts in the field and incorporate that knowledge in building an efficient pavement management decision support tool. Knowledge-based expert systems are used to develop a pavement condition forecasting model and a treatment strategy selection model. The forecasting model is capable of predicting pavement condition in the future based on both historical data and expert opinion;The treatment strategy selection model considers the forecasted condition and other inventory parameters to select feasible treatment strategies for each pavement section for all the years in the planning or analysis period. The expert system will also determine a cost and improvement in condition due to the application of the selected treatment strategy;Finally, a dynamic programming model takes the output from the treatment strategy selection knowledge based expert system and determines a list of projects and their associated treatment strategies, cost, and time to implement each. The dynamic programming model can consider different objectives functions (minimize cost or maximize benefits for example) to achieve optimal allocation of resources;This research established procedures for the integral operations of the three different models to makeup the comprehensive pavement management system. The research also established a measure of the benefits of implementing a knowledge-based expert system pavement management optimization through the evaluation of pavement condition

Risk management of groundwater pollution: a knowledge-based approach

Risk assessment and risk management now underpin environmental protection in the UK. Risk assessment provides for a structured and systematic analysis of a problem, and is an objective tool to inform risk management decisions. In particular, risk assessment can assist in the prioritisation of management activities to direct resources more effectively to significant risks. However, the application of risk assessment remains ad hoc and often focused on quantified approaches. The problem of how to integrate the results of a risk assessment into decisionmaking processes remains. The objective of this research was to assess whether a knowledgebased approach could be usefully applied to risk management decisions associated with the protection of groundwater. The use of a knowledge-based system offers considerable potential to support regulatory decision-making relating to environmental risks. Such systems utilise expert knowledge to solve specific problems as an expert would but without requiring specialist or skilled users. This research describes the development of a prototype decision-support system to assist non-specialist regulatory personnel, in the prioritisation of risks and management activities relating to groundwater threats from hydrocarbon point-sources. The research focused on the knowledge acquisition process using semi-structured interviews, concept sorting and risk rating to identify the type of information required by the expert in their decision-making processes and also to distinguish any differences of approach between experts and 'non-experts'. A conceptual model was developed that represented expert decision-making and problem solving. This model was used to develop the prototype decision-support system which was subsequently evaluated by experts and users, resulting in system refinements. A positive response to the usability and utility of the system was received from both expert and user groups, suggesting a knowledge-based approach can be usefully applied to risk management decisions associated with the protection of groundwater

TARGET: Rapid Capture of Process Knowledge

TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper