Fuzzy Logic in Clinical Practice Decision Support Systems.

James R. Warren; Gleb Beliakov; Berend-Jan van der Zwaag

doi:10.1109/HICSS.2000.926789

Fuzzy Logic in Clinical Practice Decision Support Systems.

James R. Warren, Gleb Beliakov, Berend-Jan van der Zwaag

University of South Australia, School of Computer and Information Science, Health Informatics Research Group
Deakin University, School of Computing and Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners.

Original language	English
Title of host publication	Proceedings of the Hawaii International Conference on System Sciences
Publisher	IEEE
Number of pages	10
Volume	6
ISBN (Print)	0769504930
DOIs	https://doi.org/10.1109/HICSS.2000.926789
Publication status	Published - 2000
Externally published	Yes

Keywords

fuzzy logic
decision support systems
guidelines
lakes
Australia
costs
medical expert systems
hospitals
biomedical informatics
information science

Access to Document

10.1109/HICSS.2000.926789

Cite this

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abstract = "Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners.",

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N2 - Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners.

AB - Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners.

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KW - information science

KW - fuzzy logica

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M3 - Chapter

SN - 0769504930

VL - 6

BT - Proceedings of the Hawaii International Conference on System Sciences

PB - IEEE

ER -

Fuzzy Logic in Clinical Practice Decision Support Systems.

Abstract

Keywords

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