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Related Concept Videos

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Retrieval of Mouse Oocytes
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Multiple retrieval case-based reasoning for incomplete datasets.

Nikolas Löw1, Jürgen Hesser1, Manuel Blessing1

  • 1Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.

Journal of Biomedical Informatics
|February 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for case-based reasoning (CBR) that accurately ranks similar cases even with missing data. The multiple retrieval case-based reasoning (MRCBR) system enhances decision support by handling incomplete databases effectively.

Keywords:
Case-based reasoningIncomplete dataMedical decision supportMissingness typesMultiple imputation

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Information Retrieval

Background:

  • Case-based reasoning (CBR) performance relies heavily on accurate case ranking.
  • Large databases often contain missing data, compromising ranking stability and reliability.
  • Existing CBR methods lack generalized solutions for diverse missing data conditions.

Purpose of the Study:

  • To present a novel multiple retrieval case-based reasoning (MRCBR) framework for incomplete databases.
  • To provide a statistically accurate ranking of similar cases despite missing data.
  • To offer a generalized solution applicable to various data types and missingness conditions.

Main Methods:

  • Developed a MRCBR framework integrating multiple imputation and CBR principles.
  • Ensured preservation of data distribution and database structure.
  • Tested the generalized system on numerical and categorical variables under diverse missingness conditions.

Main Results:

  • MRCBR significantly outperforms eight competing methods in handling incomplete databases.
  • The framework demonstrates reliable and stable results across various missing data scenarios.
  • MRCBR's lead over other methods increases with larger databases and more incomplete variables.

Conclusions:

  • Missing data significantly impacts CBR outcomes and cannot be ignored.
  • MRCBR offers a robust and generalized solution for accurate case ranking in incomplete databases.
  • The framework is adaptable for medical decision support and other CBR applications.