Article | Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016 | Combining data mining and text mining for detection of early stage dementia: the SAMS framework
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Title:
Combining data mining and text mining for detection of early stage dementia: the SAMS framework
Author:
Christopher Bull: School of Computing and Communications, Lancaster University, UK Dommy Asfiandy: School of Computer Science, University of Manchester, UK Ann Gledson: School of Computer Science, University of Manchester, UK Joseph Mellor: School of Computer Science, University of Manchester, UK Samuel Couth: Institute of Brain, Behaviour and Mental Health, University of Manchester, UK Gemma Stringer: Institute of Brain, Behaviour and Mental Health, University of Manchester, UK Paul Rayson: School of Computing and Communications, Lancaster University, UK Alistair Sutcliffe: School of Computing and Communications, Lancaster University, UK John Keane: School of Computer Science, University of Manchester, UK Xiaojun Zeng: School of Computer Science, University of Manchester, UK Alistair Burns: Institute of Brain, Behaviour and Mental Health, University of Manchester, UK Iracema Leroi: Institute of Brain, Behaviour and Mental Health, University of Manchester, UK Clive Ballard: Wolfson Centre for Age-Related Diseases, King’s College London, UK Pete Sawyer: School of Computing and Communications, Lancaster University, UK
Download:
Full text (pdf)
Year:
2016
Conference:
Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016
Issue:
128
Article no.:
006
Pages:
35 to 40
No. of pages:
6
Publication type:
Abstract and fulltext
Published:
2016-06-03
ISBN:
978-91-7685-730-4
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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In this paper, we describe the open-source SAMS framework whose novelty lies in bringing together both data collection (keystrokes, mouse movements, application pathways) and text collection (email, documents, diaries) and analysis methodologies. The aim of SAMS is to provide a non-invasive method for large scale collection, secure storage, retrieval and analysis of an individual’s computer usage for the detection of cognitive decline, and to infer whether this decline is consistent with the early stages of dementia. The framework will allow evaluation and study by medical professionals in which data and textual features can be linked to deficits in cognitive domains that are characteristic of dementia. Having described requirements gathering and ethical concerns in previous papers, here we focus on the implementation of the data and text collection components.

Keywords: dementia, corpus linguistics, natural language processing, data mining

Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016

Author:
Christopher Bull, Dommy Asfiandy, Ann Gledson, Joseph Mellor, Samuel Couth, Gemma Stringer, Paul Rayson, Alistair Sutcliffe, John Keane, Xiaojun Zeng, Alistair Burns, Iracema Leroi, Clive Ballard, Pete Sawyer
Title:
Combining data mining and text mining for detection of early stage dementia: the SAMS framework
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Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016

Author:
Christopher Bull, Dommy Asfiandy, Ann Gledson, Joseph Mellor, Samuel Couth, Gemma Stringer, Paul Rayson, Alistair Sutcliffe, John Keane, Xiaojun Zeng, Alistair Burns, Iracema Leroi, Clive Ballard, Pete Sawyer
Title:
Combining data mining and text mining for detection of early stage dementia: the SAMS framework
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