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Cited by 736Artificial Intelligence in HealthcareData Mining Algorithms and ApplicationsData Mining and Machine Learning Applications

The DOI matches its Crossref record; 42 of the 44 references it cites were found in a registry or an index.

Decision Trees: An Overview and Their Use in Medicine

Podgorelec, Vili, Kokol, Peter, Stiglic, Bruno, Rozman, Ivan·2002·Journal of Medical Systems
DOI 10.1023/a:1016409317640
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Abstract

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.

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Crossref · 10.1023/a:1016409317640

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Citations

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2 of the 44 references couldn’t be matched to a known paper.

Research works printed but not linked: 9

  1. Paterson, A., and Niblett, T. B., ACLS Manual, Intelligent Terminals Ltd., Edinburgh, 1982.
  2. Heath, D., Kasif, S., and Salzberg, S., k-DT: A multi-tree learning method. Proc. Second Int. Workshop Multistrategy Learn. pp. 138-149, 1993.
  3. Heath, D., Kasif, S., and Salzberg, S., Learning oblique decision trees. Proc. Thirteenth Int. Joint Conf. Artif. Intellig. (IJCAI-93) pp. 1002-1007, 1993.
  4. Rich, E., and Knight, K., Artificial Intelligence (2nd edn.), McGraw Hill, New York, 1991.
  5. Podgorelec, V., and Kokol, P., Evolutionary decision forests-decision making with multiple evolutionary constructed decision trees, Problems in Applied Mathematics and Computational Intelligence, pp. 97-103, WSES Press, 2001.
  6. Zorman, M., Kokol, P., and Podgorelec, V., Medical decision making supported by hybrid decision trees. Proc. ICSC Symp. Intellig. Syst. Appl. (ISA-2000) ICSC Academic Press, 2000.
  7. Podgorelec, V., and Kokol, P., Induction f medical decision trees with genetic algorithms. Proc. Int. ICSC Congr. Comput. Intellig. Methods Appl. (CIMA 1999) 1999.
  8. Podgorelec, V., Intelligent Systems Design and Knowledge Discovery With Automatic Programming, PhD thesis, University of Maribor, Oct. 2001.
  9. Dantchev, N., Therapeutic decision frees in psychiatry. Encephale-Revue De Psychiatrie Clinique Biologique Et Therapeutique 22(3):205-214, 1996.

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Metadata

Authors
Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I.
Affiliations
University of Maribor (SI)
Corresponding author
Podgorelec, Vili
ORCID
Venue
Journal of Medical Systems
Publisher
Springer Science and Business Media LLC
Published
2002-10
Volume
26
Issue
5
Pages
445-463
ISSN
  • 0148-5598 (ISSN-L)
  • 1573-689X
DOI
10.1023/a:1016409317640
OpenAlex
W1584935767
Cited by
736
References
44
Citations by year
2026: 39 · 2025: 83 · 2024: 76 · 2023: 80 · 2022: 62 · 2021: 64 · 2020: 48 · 2019: 46 · 2018: 36 · 2017: 26 · 2016: 21 · 2015: 29 · 2014: 25 · 2013: 24 · 2012: 20
FWCI
6.93
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of Medical Systems (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
decision tree, clinical decision-making, diagnosis, classification, knowledge representation, inductive learning, machine learning in medicine
MeSH terms
Decision Trees, Humans, United States, Clinical Medicine, Decision Support Systems, Clinical

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