Case-based medical informatics

BACKGROUND: The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge. In addition to the traditional differentiation between implicit and explicit knowledge we outline the concepts of general and individual knowledge. We connect general knowledge with the "frame problem," a fundamental issue of artificial intelligence, and individual knowledge with another important paradigm of artificial intelligence, case-based reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems. We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and that natural language processing research is an important step towards this goal that may have ethical implications for patient-centered health medicine. DISCUSSION: We focus on fundamental aspects of decision-making, which connect human expertise with individual knowledge processing. We continue with a knowledge spectrum perspective on biomedical knowledge and conclude that case-based reasoning is the paradigm that can advance towards personalized healthcare and that can enable the education of patients and providers. We center the discussion on formal methods of knowledge representation around the frame problem. We propose a context-dependent view on the notion of "meaning" and advocate the need for case-based reasoning research and natural language processing. In the context of memory based knowledge processing, pattern recognition, comparison and analogy-making, we conclude that while humans seem to naturally support the case-based reasoning paradigm (memory of past experiences of problem-solving and powerful case matching mechanisms), technical solutions are challenging. Finally, we discuss the major challenges for a technical solution: case record comprehensiveness, organization of information on similarity principles, development of pattern recognition and solving ethical issues. SUMMARY: Medical Informatics is an applied science that should be committed to advancing patient-centered medicine through individual knowledge processing. Case-based reasoning is the technical solution that enables a continuous individual knowledge processing and could be applied providing that challenges and ethical issues arising are addressed appropriately

A New Method for Matching a Document to Potential Users' Information Needs

This paper explores approaches to finding out how the information needs that a document can address can be captured. This is important in order to improve indexing strategies applied to document collections. We propose and implemented a cognitive science approach, the "jeopardy game" method of evaluation combined with "think aloud" analysis. The results of the demonstration study are presented and discussed. Some possible improvements to the method for matching a document to potential users' information needs are identified

Case-based medical informatics

Abstract Background The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge. In addition to the traditional differentiation between implicit and explicit knowledge we outline the concepts of general and individual knowledge. We connect general knowledge with the "frame problem," a fundamental issue of artificial intelligence, and individual knowledge with another important paradigm of artificial intelligence, case-based reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems. We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and that natural language processing research is an important step towards this goal that may have ethical implications for patient-centered health medicine. Discussion We focus on fundamental aspects of decision-making, which connect human expertise with individual knowledge processing. We continue with a knowledge spectrum perspective on biomedical knowledge and conclude that case-based reasoning is the paradigm that can advance towards personalized healthcare and that can enable the education of patients and providers. We center the discussion on formal methods of knowledge representation around the frame problem. We propose a context-dependent view on the notion of "meaning" and advocate the need for case-based reasoning research and natural language processing. In the context of memory based knowledge processing, pattern recognition, comparison and analogy-making, we conclude that while humans seem to naturally support the case-based reasoning paradigm (memory of past experiences of problem-solving and powerful case matching mechanisms), technical solutions are challenging. Finally, we discuss the major challenges for a technical solution: case record comprehensiveness, organization of information on similarity principles, development of pattern recognition and solving ethical issues. Summary Medical Informatics is an applied science that should be committed to advancing patient-centered medicine through individual knowledge processing. Case-based reasoning is the technical solution that enables a continuous individual knowledge processing and could be applied providing that challenges and ethical issues arising are addressed appropriately.</p

The Case for Context-Dependent Dynamic Hierarchical Representations of Knowledge in Medical Informatics

We begin this article with an introduction of associative concept spaces and their properties and reiterate an earlier proposal for a definition of Medical Informatics. We focus on dynamic representations of knowledge and contrast them with ontological approaches that imply strong commitments to particular conceptualizations of reality. This focus leads naturally to unsupervised machine learning and inductive learning that include what is known in literature as unsupervised classification and grammar induction. Next, we provide an overview of hierarchical representations and propose a unifying view. Further, we review the task of grammar induction and by means of an example we show how the task of unsupervised classification is equivalent to a grammar induction procedure complemented by the use of inverted indexes. In the last section of the article we explore the implications of context-dependent, dynamic representations of knowledge for Medical Informatics and advocate an extension of the concept of evidence in biomedicine. Finally, we acknowledge the major hurdles implied by this proposal, which consist of increased complexity and of the need for privacy and confidentiality.

In this particular example expressions such as: on(a, c), on(c, table), on(b, table), pyramid(a), brick(b), brick(c), ¬same-as(a, c), same-as(b, c), etc

<p><b>Copyright information:</b></p><p>Taken from "Case-based medical informatics"</p><p>BMC Medical Informatics and Decision Making 2004;4():19-19.</p><p>Published online 8 Nov 2004</p><p>PMCID:PMC544898.</p><p>Copyright © 2004 Pantazi et al; licensee BioMed Central Ltd.</p>, are true

Case-Based (Memory-Based) Medical Knowledge Acquisition and Processing

The &quot;frame problem&quot; is at the core of artificial intelligence and decision making research and is directly related to the intelligent agents&apos; ability to learn from experience, to the choice for knowledge representation and to the exhaustiveness of their knowledge bases. Case-based reasoning is an important paradigm in artificial intelligence, in the context of expert system development. A case-based reasoning expert comprises the case base, which can be regarded as a memory of past experiences of problem-solving, and a case matching procedure for the retrieval of cases relevant for a new problem. While humans seem to possess a natural support for these two components, this kind of knowledge acquisition and processing is not directly supported by computers