Biomedical subjects

E T Keravnou

Publications and source records attributed to E T Keravnou.

3 recordsLinked to original sources

PubMed1996

Temporal diagnostic reasoning based on time-objects.

Time is essential in diagnostic problem-solving. However, as with other commonsense tasks, time representation and reasoning is not a trivial undertaking. This probably explains why time has either been ignored or implicitly represented and used in the majority of diagnostic systems, medical or otherwise. Durations, temporal uncertainty and multiple temporal granularities are necessary requirements for medical problem-solving. Most general theories of time proposed in the literature do not address all these requirements, and some do not address any. The paper discusses time representation and reasoning in medical diagnostic problem-solving, building from a generic temporal ontology which covers the above temporal requirements. Much of what is discussed, however, is applicable to non-medical domains as well. It is argued that the diagnostic concepts (patient data, disorders, therapeutic-actions) are naturally modelled as time-objects. The resulting representation treats time as an integral dimension to these concepts, with special status. Time-object-based representations for generic hypotheses (disorders, actions) are discussed and illustrated; in the case of disorders the representation covers both an associational model and a causal-associational model. A central function of diagnostic problem-solving is deciding the compatibility of hypotheses with regard to a patient model. In this respect the paper discusses temporal and contextual screening of triggered hypotheses as well as accountings and conflicts between time-objects.

Artificial Intelligence↗

PubMed1994

Modelling diagnostic skills in the domain of skeletal dysplasias.

The diagnostic model used in the medical expert system Skeletal Dysplasias Diagnostician (SDD) is discussed. The model aims to capture the diagnostic skills of domain experts. Such skills represent high level strategies which apply across different medical domains and hence the presented model is relatively generic. Preliminary evaluation of an earlier version of the diagnostic model, which yielded promising results, has led to a considerably improved model. A sample consultation given in an appendix illustrates most aspects of the current model. The paper concludes by giving the authors' practical insights into the knowledge engineering of medical diagnostic systems.

Bone Diseases, Developmental↗

PubMedDate not supplied

Metareasoning and meta-level learning in a hybrid knowledge-based architecture.

Ahybrid knowledge-based architecture integrates different problem solvers for the same (sub)task through a control unit operating at a meta-level, the metareasoner, which coordinates the use of, and the communication between, the different problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge domain view operated by the inference mechanism in order to perform the (sub)task. Important issues in a hybrid system are the metareasoning and learning aspects. Metareasoning encompasses the functions performed by the metareasoner, while learning reflects the ability of the system to evolve on the basis of its experiences in problem solving. Learning occurs at different levels, learning at the meta-level and learning at the level of the specific problem solvers. Meta-level learning reflects the ability of the metareasoner to improve the overall performance of the hybrid system by improving the efficiency of meta-level tasks. Meta-level tasks include the initial planning of problem solving strategies and the dynamic adaptation of chosen strategies depending on new events occurring dynamically during problem solving. In this paper we concentrate on metareasoning and meta-level learning in the context of a hybrid architecture. The theoretical arguments presented in the paper are demonstrated in practice through a hybrid knowledge-based prototype system for the domain of breast cancer histopathology.

Algorithms↗