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Case-Based Reasoning for Biomedical Informatics and Medicine

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Springer Handbook of Bio-/Neuroinformatics

Part of the book series: Springer Handbooks ((SHB))

Abstract

Case-based reasoning (CBR) is an integral part of artificial intelligence. It is defined as the process of solving new problems through their comparison with similar ones with existing solutions. The CBR methodology fits well with the approach that healthcare workers take when presented with a new case, making its incorporation into a clinical setting natural. Overall, CBR is appealing in medical domains because a case base already exists, storing symptoms, diagnoses, treatments, and outcomes for each patient. Therefore, there are several CBR systems for medical diagnosis and decision support. This chapter gives an overview of CBR systems, their lifecycle, and different settings in which they appear. It also discusses major applications of CBR in the biomedical field, the methodologies used, and the systems that have been adopted. Section 13.1 provides the necessary background of CBR, while Sect. 13.2 gives an overview of techniques. Section 13.3 presents different systems in which CBR has been successfully applied, and Sect. 13.4 presents biomedical applications. A concluding discussion closes the chapter in Sect. 13.5.

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Abbreviations

ADHD:

attention-deficit hyperactivity disorder

AUC:

area under curve

C-OWL:

context ontology web language

CBR:

case-based reasoning

CPT:

continuous performance test

CT:

computer tomography

CV:

cross validation

ED:

edit distance

FP:

fixation point

KINARM:

kinesiological instrument for normal and altered reaching movement

LOD:

linked open data

MOE4CBR:

mixture of experts for CBR

NLP:

natural-language processing

PPI:

protein–protein interaction

ROC:

receiver operating characteristic

SQL:

structured query language

SRT:

saccadic reaction time

WEKA:

Waikato environment for knowledge analysis

XML:

extensible markup language

tf-idf:

term-frequency, inverse document-frequency

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Authors and Affiliations

  1. Faculty of Information (iSchool), University of Toronto, 140 St. George Street, M5S 3G6, Toronto, ONT, Canada

    Periklis Andritsos

  2. Department of Computer Science, University of Toronto, 6 Kingʼs College Road, M5S 3H5, Toronto, Ontario, Canada

    Igor Jurisica

  3. School of Computing, Queenʼs University, 557 Goodwin Hall, K7L 3N6, Kingston, ONT., Canada

    Janice I. Glasgow

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  1. Periklis Andritsos
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  2. Igor Jurisica
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  3. Janice I. Glasgow
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Correspondence to Periklis Andritsos , Igor Jurisica or Janice I. Glasgow .

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Editors and Affiliations

  1. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 120 Mayoral Drive, 1010, Auckland, New Zealand

    Nikola Kasabov

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Andritsos, P., Jurisica, I., Glasgow, J.I. (2014). Case-Based Reasoning for Biomedical Informatics and Medicine. In: Kasabov, N. (eds) Springer Handbook of Bio-/Neuroinformatics. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30574-0_13

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