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

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

Combining mouse and keyboard events with higher level desktop actions to detect mild cognitive impairment

We present a desktop monitoring application that combines keyboard, mouse, desktop and application-level activities. It has been developed to discover differences in cognitive functioning amongst older computer users indicative of mild cognitive impairment (MCI). Following requirements capture from clinical domain experts, the tool collects all Microsoft Windows events deemed potentially useful for detecting early clinical indicators of dementia, with a view to further analysis to determine the most pertinent. Further requirements capture from potential end-users has resulted in a system that has little impact on users? daily activities and ensures data security from initial recording of events through to data analysis. We describe two experiments: firstly, volunteers were asked to perform a short set of known tasks; the second (ongoing) experiment is a longitudinal study, with the software currently successfully running on participants? computers

Quantification of structural changes in the corpus callosumin children with profound hypoxic-ischaemic brain injury

Background Birth-related acute profound hypoxic–ischaemic brain injury has specific patterns of damage including the paracentral lobules. Objective To test the hypothesis that there is anatomically coherent regional volume loss of the corpus callosum as a result of this hemispheric abnormality. Materials and methods Study subjects included 13 children with proven acute profound hypoxic–ischaemic brain injury and 13 children with developmental delay but no brain abnormalities. A computerised system divided the corpus callosum into 100 segments, measuring each width. Principal component analysis grouped the widths into contiguous anatomical regions. We conducted analysis of variance of corpus callosum widths as well as support vector machine stratification into patient groups. Results There was statistically significant narrowing of the mid–posterior body and genu of the corpus callosum in children with hypoxic–ischaemic brain injury. Support vector machine analysis yielded over 95% accuracy in patient group stratification using the corpus callosum centile widths. Conclusion Focal volume loss is seen in the corpus callosum of children with hypoxic–ischaemic brain injury secondary to loss of commissural fibres arising in the paracentral lobules. Support vector machine stratification into the hypoxic–ischaemic brain injury group or the control group on the basis of corpus callosum width is highly accurate and points towards rapid clinical translation of this technique as a potential biomarker of hypoxic–ischaemic brain injur

Which computer-use behaviours are most indicative of cognitive decline? Insights from an expert reference group

Computer use is becoming ubiquitous amongst older adults. As computer-use depends on complex cognitive functions, measuring individuals’ computer-use behaviours over time may provide a way to detect changes in their cognitive functioning. However, it is uncertain which computer-use behaviour changes are most likely to be associated with declines of particular cognitive functions. To address this, we convened six experts from clinical and cognitive neurosciences to take part in two workshops and a follow-up survey to gain consensus on which computer-use behaviours would likely be the strongest indicators of cognitive decline. This resulted in a list of twenty-one computer-use behaviours that the majority of experts agreed would offer a ‘strong indication’ of decline in a specific cognitive function, across Memory, Executive function, Language, and Perception and Action domains. This list enables a hypothesis-driven approach to analysing computer-use behaviours predicted to be markers of cognitive decline

Known and unknown requirements in healthcare

We report experience in requirements elicitation of domain knowledge from experts in clinical and cognitive neurosciences. The elicitation target was a causal model for early signs of dementia indicated by changes in user behaviour and errors apparent in logs of computer activity. A Delphi-style process consisting of workshops with experts followed by a questionnaire was adopted. The paper describes how the elicitation process had to be adapted to deal with problems encountered in terminology and limited consensus among the experts. In spite of the difficulties encountered, a partial causal model of user behavioural pathologies and errors was elicited. This informed requirements for configuring data- and text-mining tools to search for the specific data patterns. Lessons learned for elicitation from experts are presented, and the implications for requirements are discussed as “unknown unknowns”, as well as configuration requirements for directing data-/text-mining tools towards refining awareness requirements in healthcare applications

Measuring Topic Homogeneity and its Application to Dictionary-based Word Sense Disambiguation

The use of topical features is abundant in Natural Language Processing (NLP), a major example being in dictionary-based Word Sense Disambiguation (WSD). Topic features rely on the context of a target word, but although the role of context has been discussed as an 'open problem' in the WSD literature, the nature of context, and how it might vary between different styles of documents has been largely ignored.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Measuring Topic Homogeneity and its Application to Dictionary-based Word Sense Disambiguation

The use of topical features is abundant in Natural Language Processing (NLP), a major example being in dictionary-based Word Sense Disambiguation (WSD). Topic features rely on the context of a target word, but although the role of context has been discussed as an 'open problem' in the WSD literature, the nature of context, and how it might vary between different styles of documents has been largely ignored.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Collaborations Workshop 2018 - Lightning talk - Ann Gledson

Presentation during Collaborations Workshop 2018, https://www.software.ac.uk/cw18/