Development of decision support models for nursing diagnoses, using the fuzzy maximum-minimum composition

2.50
Hdl Handle:
http://hdl.handle.net/10755/153796
Type:
Presentation
Title:
Development of decision support models for nursing diagnoses, using the fuzzy maximum-minimum composition
Abstract:
Development of decision support models for nursing diagnoses, using the fuzzy maximum-minimum composition
Conference Sponsor:Sigma Theta Tau International
Conference Year:2011
Author:Jensen, Rodrigo, MNSc, RN
P.I. Institution Name:University of Campinas
Title:Doctoral Student
[22nd International Nursing Research Congress - Research Presentation] Purpose: Describe the development of decision support models for nursing diagnoses, using the fuzzy maximum-minimum composition.
Methods: In developing a model is constructed, in a worksheet, a matrix where are distributed the variables (signs and symptoms) in the lines, that may be associated with the diagnoses in question. In columns are described the diagnostics. An expert, or panel of experts, should determine the level of relationship between each variable and each diagnosis, assigning values from 0 (no relation) to 1 (total relation). In a second worksheet is constructed a new matrix, using the same variables (signs and symptoms). In this second matrix, nurses to use the model, determines how strong the intensity of the signs and symptoms in patients by assigning values between 0 (absent) and 1 (strongly present). The analysis in the model is similar to a multiplication of matrices. At the end of this process, is defining a matrix whose elements are the degrees of fuzzy possibility for each diagnosis. The decision process is completed by choosing the maximum value of the distribution of diagnostic possibilities to determine the final diagnosis.
Results: The model is simple and requires few computing resources for software's development. In our experience, the maximum-minimum composition was used to develop a model that differentiates diagnoses related to urinary elimination (Lopes et al., 2009), with good results.
Conclusion: The fuzzy maximum-minimum composition can be used to develop models that support decision making for complex nursing diagnoses, which would need a second opinion given by expert.
References: Lopes MHBM, Ortega NRS, Massad E, Marin HF. Model for differential nursing diagnosis of alterations in urinary elimination based on fuzzy logic. Comput Inform Nurs. 2009;27(5):324-9.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleDevelopment of decision support models for nursing diagnoses, using the fuzzy maximum-minimum compositionen_GB
dc.identifier.urihttp://hdl.handle.net/10755/153796-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Development of decision support models for nursing diagnoses, using the fuzzy maximum-minimum composition</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2011</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Jensen, Rodrigo, MNSc, RN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Campinas</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Doctoral Student</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">rodrigojensen@yahoo.com.br</td></tr><tr><td colspan="2" class="item-abstract">[22nd International Nursing Research Congress - Research Presentation] Purpose:&nbsp;Describe the development of decision support models for nursing diagnoses, using the fuzzy maximum-minimum composition. <br/>Methods: In developing a model is constructed, in a worksheet, a matrix where are distributed the variables (signs and symptoms) in the lines, that may be associated with the diagnoses in question. In columns are described the diagnostics. An expert, or panel of experts, should determine the level of relationship between each variable and each diagnosis, assigning values from 0 (no relation) to 1 (total relation). In a second worksheet is constructed a new matrix, using the same variables (signs and symptoms). In this second matrix, nurses to use the model, determines how strong the intensity of the signs and symptoms in patients by assigning values between 0 (absent) and 1 (strongly present). The analysis in the model is similar to a multiplication of matrices. At the end of this process, is defining a matrix whose elements are the degrees of fuzzy possibility for each diagnosis. The decision process is completed by choosing the maximum value of the distribution of diagnostic possibilities to determine the final diagnosis. <br/>Results: The model is simple and requires few computing resources for software's development. In our experience, the maximum-minimum composition was used to develop a model that differentiates diagnoses related to urinary elimination (Lopes et al., 2009), with good results. <br/>Conclusion: The fuzzy maximum-minimum composition can be used to develop models that support decision making for complex nursing diagnoses, which would need a second opinion given by expert. <br/>References: Lopes MHBM, Ortega NRS, Massad E, Marin HF. Model for differential nursing diagnosis of alterations in urinary elimination based on fuzzy logic. Comput Inform Nurs. 2009;27(5):324-9.</td></tr></table>en_GB
dc.date.available2011-10-26T12:31:22Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T12:31:22Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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