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Case-Based Reasoning for Biomedical Informatics and Medicine

  • Periklis Andritsos
  • Igor Jurisica
  • Janice I. Glasgow

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.

Keywords

Feature Selection Case Base Edit Distance Continuous Performance Test Saccadic Reaction Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Faculty of Information (iSchool)University of TorontoTorontoCanada
  2. 2.Department of Computer ScienceUniversity of TorontoTorontoCanada
  3. 3.School of ComputingQueenʼs UniversityKingstonCanada

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