Using multiobjective evolutionary algorithms to understand Parkinson's disease

Marta Vallejo; Jeremy Cosgrove; Jane E. Alty; Stephen L. Smith; David W. Corne; Michael A. Lones

doi:10.1145/2908961.2909026

Using multiobjective evolutionary algorithms to understand Parkinson's disease

Marta Vallejo, Jeremy Cosgrove, Jane E. Alty, Stephen L. Smith, David W. Corne, Michael A. Lones^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The incidence of neurodegenerative diseases such as Parkinson's is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algorithms to explore recordings of patients drawing neurological assessment figures, with the aim of identifying patterns of cognitive and motor signals that discriminate different disease states. As a proof of principle, we demonstrate how this approach can be used to explore the trade-off between predicting clinical measures of motor and cognitive deficit.

Original language	English
Title of host publication	GECCO 2016 Companion
Subtitle of host publication	Proceedings of the 2016 Genetic and Evolutionary Computation Conference
Publisher	Association for Computing Machinery
Pages	13-14
Number of pages	2
ISBN (Electronic)	9781450343237
DOIs	https://doi.org/10.1145/2908961.2909026
Publication status	Published - 2016
Event	Genetic and Evolutionary Computation Conference 2016 - Denver, United States Duration: 20 Jul 2016 → 24 Jul 2016

Conference

Conference	Genetic and Evolutionary Computation Conference 2016
Abbreviated title	GECCO 2016
Country/Territory	United States
City	Denver
Period	20/07/16 → 24/07/16

Keywords

Multi-objective evolutionary algorithms
Parkinson's disease
Polynomial regression
Predictive modelling

ASJC Scopus subject areas

Software
Computer Science Applications
Computational Theory and Mathematics

Access to Document

10.1145/2908961.2909026

Cite this

@inproceedings{220452c3c84e4c80a730b228a8c329d4,

title = "Using multiobjective evolutionary algorithms to understand Parkinson's disease",

abstract = "The incidence of neurodegenerative diseases such as Parkinson's is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algorithms to explore recordings of patients drawing neurological assessment figures, with the aim of identifying patterns of cognitive and motor signals that discriminate different disease states. As a proof of principle, we demonstrate how this approach can be used to explore the trade-off between predicting clinical measures of motor and cognitive deficit.",

keywords = "Multi-objective evolutionary algorithms, Parkinson's disease, Polynomial regression, Predictive modelling",

author = "Marta Vallejo and Jeremy Cosgrove and Alty, \{Jane E.\} and Smith, \{Stephen L.\} and Corne, \{David W.\} and Lones, \{Michael A.\}",

year = "2016",

doi = "10.1145/2908961.2909026",

language = "English",

pages = "13--14",

booktitle = "GECCO 2016 Companion",

publisher = "Association for Computing Machinery",

address = "United States",

note = "Genetic and Evolutionary Computation Conference 2016, GECCO 2016 ; Conference date: 20-07-2016 Through 24-07-2016",

}

Vallejo, M, Cosgrove, J, Alty, JE, Smith, SL, Corne, DW & Lones, MA 2016, Using multiobjective evolutionary algorithms to understand Parkinson's disease. in GECCO 2016 Companion: Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, pp. 13-14, Genetic and Evolutionary Computation Conference 2016, Denver, United States, 20/07/16. https://doi.org/10.1145/2908961.2909026

Using multiobjective evolutionary algorithms to understand Parkinson's disease. / Vallejo, Marta; Cosgrove, Jeremy; Alty, Jane E. et al.
GECCO 2016 Companion: Proceedings of the 2016 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2016. p. 13-14.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Using multiobjective evolutionary algorithms to understand Parkinson's disease

AU - Vallejo, Marta

AU - Cosgrove, Jeremy

AU - Alty, Jane E.

AU - Smith, Stephen L.

AU - Corne, David W.

AU - Lones, Michael A.

PY - 2016

Y1 - 2016

N2 - The incidence of neurodegenerative diseases such as Parkinson's is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algorithms to explore recordings of patients drawing neurological assessment figures, with the aim of identifying patterns of cognitive and motor signals that discriminate different disease states. As a proof of principle, we demonstrate how this approach can be used to explore the trade-off between predicting clinical measures of motor and cognitive deficit.

AB - The incidence of neurodegenerative diseases such as Parkinson's is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algorithms to explore recordings of patients drawing neurological assessment figures, with the aim of identifying patterns of cognitive and motor signals that discriminate different disease states. As a proof of principle, we demonstrate how this approach can be used to explore the trade-off between predicting clinical measures of motor and cognitive deficit.

KW - Multi-objective evolutionary algorithms

KW - Parkinson's disease

KW - Polynomial regression

KW - Predictive modelling

UR - https://www.scopus.com/pages/publications/84986293305

U2 - 10.1145/2908961.2909026

DO - 10.1145/2908961.2909026

M3 - Conference contribution

AN - SCOPUS:84986293305

SP - 13

EP - 14

BT - GECCO 2016 Companion

PB - Association for Computing Machinery

T2 - Genetic and Evolutionary Computation Conference 2016

Y2 - 20 July 2016 through 24 July 2016

ER -

Using multiobjective evolutionary algorithms to understand Parkinson's disease

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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