Combining data mining and text mining for detection of early stage dementia: the SAMS framework

Christopher Neil Bull, Dommy Asfiandy, Ann Gledson, Joseph Mellor, Samuel Couth, Gemma Stringer, Paul Edward Rayson, Alistair Gordon Simpson Sutcliffe, John Keane, Xiao-Jun Zeng, Alistair Burns, Iracema Leroi, Clive Ballard, Peter Harvey Sawyer

Research output: Chapter in Book/Published conference outputChapter


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.
Original languageEnglish
Title of host publicationResources and ProcessIng of linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric impairments (RaPID '16) workshop
Number of pages6
Publication statusPublished - 23 May 2016

Bibliographical note

Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License


  • Dementia
  • Corpus Linguistics
  • Natural Language Processing
  • Data Mining


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