Two Case-Based Systems for Explaining Exceptions in Medicine

Two Case-Based Systems for Explaining Exceptions in Medicine

Rainer Schmidt (University of Rostock, Germany)
DOI: 10.4018/978-1-60566-218-3.ch011
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In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal with them appropriately. In medical studies and in research, exceptions shall be explained. In this chapter, we present two systems that deal with both sorts of these situations. The first one, called ISOR-1, is a knowledge-based system for therapy support. It does not just compute therapy recommendations, but it especially investigates therapy inefficacy. The second system, ISOR-2, is designed for medical studies or research. It helps to explain cases that contradict a theoretical hypothesis. Both systems are working in close co-operation with the user, who is not just considered as knowledge provider to build the system but is incorporated as additional knowledge source at runtime. Within a dialogue between the doctor and the system solutions respectively explanations are searched.
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Isor-1: Investigating Therapy Inefficacy

In medical practice, therapies prescribed according to a certain diagnosis sometimes do not give desired results. Sometimes therapies are effective for some time but then suddenly stop helping any more. There are many possible reasons. A diagnosis might be erroneous, the state of a patient might have changed completely, or the state might have changed just slightly but with important implications for an existing therapy. Furthermore, a patient might have caught an additional disease, some other complication might have occurred, a patient might have changed his/her lifestyle (e.g. started a diet) and so on.

For long-term therapy support, especially in the endocrine domain and in psychiatry, we have developed a Case-Based Reasoning (CBR) system, named ISOR-1, which not only performs typical therapeutic tasks but also especially deals with situations where therapies have become ineffective. Therefore, it first attempts to find causes for inefficacy and subsequently computes new therapy recommendations that should perform better than those administered before.ISOR-1 is a medical Case-Based Reasoning system that deals with the following tasks:

  • Choose appropriate (initial) therapies

  • Compute doses for chosen therapies

  • Update dose recommendations according to laboratory test results

  • Establish new doses of prescribed medicine according to changes in a patient’s medical status or lifestyle

  • Find out probable reasons why administered therapies are not as efficient as they should be

  • Test obtained reasons for inefficacy and make sure that they are the real cause

  • Suggest recommendations to avoid inefficacy of prescribed therapies

ISOR-1 deals with long-term diseases, e.g. psychiatric diseases, and with diseases even lasting for a lifetime, e.g. endocrine malfunctions.

For psychiatric diseases some Case-Based Reasoning systems have been developed, which deal with specific diseases or problems, e.g. with Alzheimer’s disease (Marling and Whitehouse 2001) or with eating disorders (Bichindaritz 1994). Since we do not want to discuss various psychiatric problems but intend to illustrate ISOR by understandable examples, in this chapter we mainly focus on some endocrine and psychiatric disorders, namely on hypothyroidism and depressive symptoms. Inefficacy of pharmacological therapy for depression is a widely known problem (e.g. Hirschfeld, 2002; Cuffel, 2003). There are many approaches to solve this problem. Guidelines and algorithms have been created (e.g. Alacorn, 20008; Osser, Patterson, 1998). ISOR gives reference to a psychopharmacology algorithm (Osser, Patterson, 1998) that is available on the website htp://

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Table of Contents
Riccardo Bellazzi
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Chapter 1
Jana Zvárová, Arnošt Veselý
This chapter introduces the basic concepts of medical informatics: data, information, and knowledge. Data are classified into various types and... Sample PDF
Data, Information and Knowledge
Chapter 2
Michel Simonet, Radja Messai, Gayo Diallo
Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status... Sample PDF
Ontologies in the Health Field
Chapter 3
Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to... Sample PDF
Cost-Sensitive Learning in Medicine
Chapter 4
Arnošt Veselý
This chapter deals with applications of artificial neural networks in classification and regression problems. Based on theoretical analysis it... Sample PDF
Classification and Prediction with Neural Networks
Chapter 5
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Preprocessing Perceptrons and Multivariate Decision Limits
Chapter 6
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Chapter 7
ECG Processing  (pages 137-160)
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ECG Processing
Chapter 8
EEG Data Mining Using PCA  (pages 161-180)
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EEG Data Mining Using PCA
Chapter 9
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Chapter 10
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Chapter 11
Rainer Schmidt
In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal... Sample PDF
Two Case-Based Systems for Explaining Exceptions in Medicine
Chapter 12
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Mining Tuberculosis Data  (pages 332-349)
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Chapter 17
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