Finite Mixture Models for Analysing Epileptic Seizure Count Data.
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| Title: | Finite Mixture Models for Analysing Epileptic Seizure Count Data. |
|---|---|
| Authors: | Adegboye, Oyelola A.1 |
| Source: | Annual International Conference on Computational Mathematics, Computational Geometry & Statistics. 2016, p233-236. 4p. |
| Subjects: | Epilepsy research, Treatment of epilepsy, Finite mixture models (Statistics), Probability density function, Poisson distribution |
| Abstract: | This study presents the use of finite mixture for analyzing epileptic seizure counts which allows for modeling inhomogeneous populations with a different probability density function in each component. Epilepsy is a disease that is often misunderstood thus leading to fear, secrecy, stigmatization and the risk of social and legal penalties. It is widely characterized by the spontaneous and unforeseeable occurrence of seizures during which the perception or behavior of patients is disturbed. A finite mixture has a finite number of components; it offers a simple and natural model for unobserved population heterogeneity under the condition that number of unobserved subpopulation is fixed. In this study thirteen component Poisson mixtures were obtained and patients were classified into different sub-populations. There was no patient classified into the seventh sub-population. Poisson regression indicates significant interaction between baseline rate and sup-population, and treatment and sup-population. [ABSTRACT FROM AUTHOR] |
| Copyright of Annual International Conference on Computational Mathematics, Computational Geometry & Statistics is the property of Global Science & Technology Forum 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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| Items | – Name: Title Label: Title Group: Ti Data: Finite Mixture Models for Analysing Epileptic Seizure Count Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Adegboye%2C+Oyelola+A%2E%22">Adegboye, Oyelola A.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annual+International+Conference+on+Computational+Mathematics%2C+Computational+Geometry+%26+Statistics%22">Annual International Conference on Computational Mathematics, Computational Geometry & Statistics</searchLink>. 2016, p233-236. 4p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Epilepsy+research%22">Epilepsy research</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+of+epilepsy%22">Treatment of epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+mixture+models+%28Statistics%29%22">Finite mixture models (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Poisson+distribution%22">Poisson distribution</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study presents the use of finite mixture for analyzing epileptic seizure counts which allows for modeling inhomogeneous populations with a different probability density function in each component. Epilepsy is a disease that is often misunderstood thus leading to fear, secrecy, stigmatization and the risk of social and legal penalties. It is widely characterized by the spontaneous and unforeseeable occurrence of seizures during which the perception or behavior of patients is disturbed. A finite mixture has a finite number of components; it offers a simple and natural model for unobserved population heterogeneity under the condition that number of unobserved subpopulation is fixed. In this study thirteen component Poisson mixtures were obtained and patients were classified into different sub-populations. There was no patient classified into the seventh sub-population. Poisson regression indicates significant interaction between baseline rate and sup-population, and treatment and sup-population. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annual International Conference on Computational Mathematics, Computational Geometry & Statistics is the property of Global Science & Technology Forum 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: 10.5176/2251-1911_CMCGS16.30 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 233 Subjects: – SubjectFull: Epilepsy research Type: general – SubjectFull: Treatment of epilepsy Type: general – SubjectFull: Finite mixture models (Statistics) Type: general – SubjectFull: Probability density function Type: general – SubjectFull: Poisson distribution Type: general Titles: – TitleFull: Finite Mixture Models for Analysing Epileptic Seizure Count Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Adegboye, Oyelola A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 22511911 Titles: – TitleFull: Annual International Conference on Computational Mathematics, Computational Geometry & Statistics Type: main |
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