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

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived

MED1: ein heuristisches Diagnosesystem mit effizienter Kontrollstruktur

MED1 [Meta-Ebenen-Diagnosesystem] ist ein vollständig implementiertes Werkzeug zur Erstellung von insbesondere medizinischen Diagnosesystemen. Die Vorgehensweise des Systems, die der Benutzer mit der Erklärungskomponente nachvollziehen kann, orientiert sich so weit wie möglich an der eines Arztes. Es unterscheidet sich von den übrigen Diagnosesystemen, die wie MED1 auf den Methoden der künstlichen Intelligenz zur Repräsentation von heuristischem Expertenwissen aufbauen, vor allem durch mehr Flexibilität bei der Verdachtsgenerierung‚ bei der Auswertung von unsicheren Daten und bei der Indikation von technischen Untersuchungen. Die umfangreiche Knowledge-Acquisition-Komponente gestattet es dem Experten, der mit der Vorgehensweise von MED1 vertraut ist, sein Wissen dem System auch ohne Kenntnisse von LISP einzugeben.MED1 [Meta-Ebenen-Diagnosesystem or Meta-level-diagnosis-system] is a fully implemented tool for constructing diagnosis systems especially in the medical domain. It tries to simulate the way physicians diagnose and is capable of explaining every reasoning-step. From the other diagnosis systems using the techniques of Artificial Intelligence for representation of heuristic expert knowledge MED1 differs mainly regarding more flexibility in hypothesis generation, reasoning with uncertainty and deciding the necessity of technical procedures. The comfortable knowledge-acquisition facility enables the expert familiar with the structure of MED1 to communicate his knowledge to the system without knowing anything about LISP

A neural networks based approach to knowledge aquistion and expert systems

[[abstract]]Often a major difficulty in the design of expert systems is the process of acquiring the requisite knowledge in the form of production rules. This paper presents a novel class of neural networks which are trained in such a way that they provide an appealing solution to the problem of knowledge acquisition. The value of the network parameters, after sufficient training, are then utilized to generate production rules on the basis of preselected meaningful coordinates. Further, the paper provides a mathematical framework for achieving reasonable generalization properties via an appropriate training algorithm (supervised decision-directed learning) with a structure that provides acceptable knowledge representations of the data, The concepts and methods presented in the paper are illustrated through one practical example from medical diagnosis.[[conferencetype]]國際[[conferencedate]]19931017~19931020[[booktype]]紙本[[conferencelocation]]Le Touquet, Franc

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

Elicitation and representation of expert knowledge for computer aided diagnosis in mammography

To study how professional radiologists describe, interpret and make decisions about micro-calcifications in mammograms. The purpose was to develop a model of the radiologists' decision making for use in CADMIUM II, a computerized aid for mammogram interpretation that combines symbolic reasoning with image processing

A Model for an Intelligent Support Decision System in Aquaculture

The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system.support decision system, diagnosis, multi-agent system, fish diseases