Knowledge reuse among diagnostic problem-solving methods in the Shell-Kit D3

2007

Appl Intell

Self Cite

In general, cases capture knowledge and concrete experiences of specific situations. By exploiting case-based knowledge for characterizing a subgroup pattern, additional information about the subgroup objects can be provided. This paper proposes a case-based approach for characterizing and analyzing subgroup patterns: It presents techniques for retrieving characteristic factors and a set of corresponding cases for the inspection and analysis of a specific subgroup pattern. Then, the set of factors and cases are merged into prototypical cases for presentation to the user. Such an alternative view on the subgroup pattern provides important introspective information on the subgroup objects, that is, the cases covered by the subgroup description: Using drill-down techniques, the user can perform a detailed introspection of a subgroup pattern using prototypical pattern cases. Additionally, these enable a convenient retrieval of interesting (meta-)information associated with the respective subgroup objects.

Section: Characterizing Subgroup Patterns In the Context Of Knowledgementioning

confidence: 99%

A case-based approach for characterization and analysis of subgroup patterns

2007

Appl Intell

Self Cite

“…SONOCONSULT is a knowledge-based documentation and consultation system for sonography, an advanced and isolated part of HEPATOCONSULT [4], developed and maintained by the domain specialists using the shell-kit D3 [5]. The quality of the derived diagnoses usually is very good as checked by experts in a medical evaluation, cf.…”

Section: Properties Of the Case Basementioning

confidence: 99%

“…Our evaluation is based on cases collected by the knowledge-based documentation and consultation system for sonography SONOCONSULT [3] (an advanced and isolated part of HEPATOCONSULT [4]), built with the diagnostic shell kit D3 [5]. This system is in routine use in the DRK-hospital in Berlin/Köpenick.…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic learning of simple diagnostic scores utilizing complexity measures

Baumeister

Artificial Intelligence in Medicine

2006

Self Cite

Objective: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semiautomatic learning methods can be used to support the domain specialists. They are usually not only interested in the accuracy of the learned knowledge: the understandability and interpretability of the learned models is of prime importance as well. Then, often simple models are more favorable than complex ones. Methods and Material: We propose diagnostic scores as a promising approach for the representation of simple diagnostic knowledge, and present a method for inductive learning of diagnostic scores. It can be incrementally refined by including background knowledge. We present complexity measures for determining the complexity of the learned scores. Results: We give an evaluation of the presented approach using a case base from the fielded system SONOCONSULT. We further discuss that the user can easily balance between accuracy and complexity of the learned knowledge applying the presented measures. Conclusions: We argue that semi-automatic learning methods can support the domain specialist efficiently when building (diagnostic) knowledge systems from scratch. The presented complexity measures allow for an intuitive assessment of the learned patterns.

“…Our implementation and evaluation is based on the knowledge-based documentation and consultation system for sonography SONOCONSULT (an advanced and isolated part of HEPATOCONSULT [3]) based on the diagnostic shell kit D3 [4]. This system is in routine use in the DRK-hospital in Berlin/Köpenick.…”

Section: Introductionmentioning

confidence: 99%

Inductive Learning of Simple Diagnostic Scores

Baumeister

2003

Medical Data Analysis

Self Cite

Abstract. Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semi-automatic learning methods can be used to support the expert. We propose diagnostic scores as a promising approach and present a method for inductive learning of diagnostic scores. It can be be refined incrementally by applying different types of background knowledge. We give an evaluation of the presented approach with a real-world case base.