Developing the Function of ‘Magnitude-of-Effect’ (MoE) for Artificial Neural Networks to Demonstrate the Causal Effect of Exposure Variables on Outcome Variable.
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| Title: | Developing the Function of ‘Magnitude-of-Effect’ (MoE) for Artificial Neural Networks to Demonstrate the Causal Effect of Exposure Variables on Outcome Variable. |
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| Authors: | Moayed, Farman A.1 farman.moayed@indstate.edu, Shell, Richard L.2 |
| Source: | Annals of Occupational Hygiene. Mar2011, Vol. 55 Issue 2, p143-151. 9p. |
| Subjects: | Artificial neural networks, Quantitative research, Logistic regression analysis, Occupational disease risk factors, Industrial safety, Safety engineers, Research methodology, Occupational diseases, Mathematical variables, Occupational hazards, Environmental exposure |
| Abstract: | Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used by safety professionals and practitioners to assess the association between exposures and possible occupational disorders or diseases and predict the outcome. Recently, artificial neural network (ANN) models are gradually finding their way into safety field. It has been shown that they are capable of predicting outcomes more accurately than LR, but they are incapable of demonstrating the direct correlation between exposure variables and a possible outcome variable. The objective of this study was to develop a mathematical function that can use the result of ANN models to produce a measure for evaluating the direct association between exposure and possible outcome variables. This function was referred to as the function of Magnitude-of-Effect (MoE). Safety experts and practitioners can use the MoE function to interpret how strongly an exposure variable can affect the outcome variable, similar to an odds ratio, which can be calculated by using estimated parameters in LR models. The significance of such achievement is that it can eliminate one of the ANN model’s shortcoming and make them more applicable in the occupational safety and health engineering field. [ABSTRACT FROM PUBLISHER] |
| Copyright of Annals of Occupational Hygiene is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 58150045 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Developing the Function of ‘Magnitude-of-Effect’ (MoE) for Artificial Neural Networks to Demonstrate the Causal Effect of Exposure Variables on Outcome Variable. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moayed%2C+Farman+A%2E%22">Moayed, Farman A.</searchLink><relatesTo>1</relatesTo><i> farman.moayed@indstate.edu</i><br /><searchLink fieldCode="AR" term="%22Shell%2C+Richard+L%2E%22">Shell, Richard L.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Occupational+Hygiene%22">Annals of Occupational Hygiene</searchLink>. Mar2011, Vol. 55 Issue 2, p143-151. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+disease+risk+factors%22">Occupational disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+safety%22">Industrial safety</searchLink><br /><searchLink fieldCode="DE" term="%22Safety+engineers%22">Safety engineers</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+diseases%22">Occupational diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+variables%22">Mathematical variables</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+hazards%22">Occupational hazards</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+exposure%22">Environmental exposure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used by safety professionals and practitioners to assess the association between exposures and possible occupational disorders or diseases and predict the outcome. Recently, artificial neural network (ANN) models are gradually finding their way into safety field. It has been shown that they are capable of predicting outcomes more accurately than LR, but they are incapable of demonstrating the direct correlation between exposure variables and a possible outcome variable. The objective of this study was to develop a mathematical function that can use the result of ANN models to produce a measure for evaluating the direct association between exposure and possible outcome variables. This function was referred to as the function of Magnitude-of-Effect (MoE). Safety experts and practitioners can use the MoE function to interpret how strongly an exposure variable can affect the outcome variable, similar to an odds ratio, which can be calculated by using estimated parameters in LR models. The significance of such achievement is that it can eliminate one of the ANN model’s shortcoming and make them more applicable in the occupational safety and health engineering field. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of Occupational Hygiene is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/annhyg/meq080 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 143 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Quantitative research Type: general – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Occupational disease risk factors Type: general – SubjectFull: Industrial safety Type: general – SubjectFull: Safety engineers Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Occupational diseases Type: general – SubjectFull: Mathematical variables Type: general – SubjectFull: Occupational hazards Type: general – SubjectFull: Environmental exposure Type: general Titles: – TitleFull: Developing the Function of ‘Magnitude-of-Effect’ (MoE) for Artificial Neural Networks to Demonstrate the Causal Effect of Exposure Variables on Outcome Variable. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moayed, Farman A. – PersonEntity: Name: NameFull: Shell, Richard L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 00034878 Numbering: – Type: volume Value: 55 – Type: issue Value: 2 Titles: – TitleFull: Annals of Occupational Hygiene Type: main |
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