Modelling medical diagnostic processes

The thesis investigates the development of medical reasoning processes and how student modelling of such processes can be achieved in intelligent tutoring systems. The domain of orthopaedics was chosen for the research. Literature has shown that medical reasoning has been modelled mainly from an expert point of view. The research problem addressed is to model explicitly various levels of medical expertise in terms of reasoning strategies. The thesis reports on a system, DEMEREST (DEvelopment of MEdical REasoning STrategies), a developmental user model component which describes successive stages of medical reasoning and which could ultimately be part of a medical tutor. The system diagnoses physicians' reasoning strategies, determines the level of expertise and produces a plan corresponding to the application of these strategies. As a basis of doing so, a set of seven reasoning strategies was identified in the medical problem solving literature. These strategies are based on generalisation, specialisation, confirmation, elimination, problem refinement, hypothesis generation and anatomy. An empirical study was carried out to examine the development of these strategies. Protocols of ten physicians at various levels of expertise were collected and analysed. A number of interactions of strategies at different levels of expertise was identified in half of these protocols and this information was used to construct a model of changes of strategies over time. Planning in· artificial intelligence was used as a means of decomposing medical problem solving into a set of goals; the goals being associated with the reasoning strategies. By taking this approach, medical reasoning is viewed as a planning process. The remaining protocols from the empirical study were used to evaluate DEMEREST. The system was tested for its ability to determine a level of expertise for each protocol, model the reasoning strategies applied and their interactions, and generate a plan for each protocol. The assessment of the overall performance of the system showed that it was successful. This assessment also helped to identify conceptual as well as implementation constraints of the prototype system. The main result of the research undertaken in this thesis is that the design of the system DEMEREST demonstrates the feasibility of modelling the development of medical reasoning strategies and its usefulness for student modelling

Ontology based contextualization and context constraints management in web service processes

The flexibility and dynamism of service-based applications impose shifting the validation process to runtime; therefore, runtime monitoring of dynamic features attached to service-based systems is becoming an important direction of research that motivated the definition of our work. We propose an ontology based contextualization and a framework and techniques for managing context constraints in a Web service process for dynamic requirements validation monitoring at process runtime. Firstly, we propose an approach to define and model dynamic service context attached to composition and execution of services in a service process at run-time. Secondly, managing context constraints are defined in a framework, which has three main processes for context manipulation and reasoning, context constraints generation, and dynamic instrumentation and validation monitoring of context constraints. The dynamic requirements attached to service composition and execution are generated as context constraints. The dynamic service context modeling is investigated based on empirical analysis of application scenarios in the classical business domain and analysing previous models in the literature. The orientation of context aspects in a general context taxonomy is considered important. The Ontology Web Language (OWL) has many merits on formalising dynamic service context such as shared conceptualization, logical language support for composition and reasoning, XML based interoperability, etc. XML-based constraint representation is compatible with Web service technologies. The analysis of complementary case study scenarios and expert opinions through a survey illustrate the validity and completeness of our context model. The proposed techniques for context manipulation, context constraints generation, instrumentation and validation monitoring are investigated through a set of experiments from an empirical evaluation. The analytical evaluation is also used to evaluate algorithms. Our contributions and evaluation results provide a further step towards developing a highly automated dynamic requirements management system for service processes at process run-time

A literature review of expert problem solving using analogy

We consider software project cost estimation from a problem solving perspective. Taking a cognitive psychological approach, we argue that the algorithmic basis for CBR tools is not representative of human problem solving and this mismatch could account for inconsistent results. We describe the fundamentals of problem solving, focusing on experts solving ill-defined problems. This is supplemented by a systematic literature review of empirical studies of expert problem solving of non-trivial problems. We identified twelve studies. These studies suggest that analogical reasoning plays an important role in problem solving, but that CBR tools do not model this in a biologically plausible way. For example, the ability to induce structure and therefore find deeper analogies is widely seen as the hallmark of an expert. However, CBR tools fail to provide support for this type of reasoning for prediction. We conclude this mismatch between experts’ cognitive processes and software tools contributes to the erratic performance of analogy-based prediction

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Comment: Expert Elicitation for Reliable System Design

Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]Comment: Published at http://dx.doi.org/10.1214/088342306000000547 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel