Chasing Vapors within a Disappearing Mist: Conceptualizing Dementia Narratives

The built environment within healthcare institutions is of critical importance to persons with dementia, as the characteristics of the interior environment, the lived experience within, and the reciprocal nature of that exchange can be directly related to their well being. Yet the role of the environmentand more importantly, the role of the patient as a primary author towards conceptions of what that physical environment should look and feel likerarely feature in routine dementia patient satisfaction assessments. This research sought to understand whether patients with dementia have the capacity to perceive the institutional space and place around them, and if so, how. Participants with mild to moderate dementia living in an institutional setting who could provide consent were asked a number of lived experience questions. The responses were videotaped and scored qualitatively. The results suggest that patients with dementia are aware of the institutional space around them, and can be active agents when contributing to thoughtfully designed environments that promote the health and well being of its residents. If persons with dementia are thought of as active participants within the design of the built environment, then this can lead to new reconceptualization of spatial domains and ultimately impact care

The Genitive Ratio and its Applications

The genitive ratio (GR) is a novel method of classifying nouns as animate, concrete or abstract. English has two genitive (possessive) constructions: possessive-s (the boy's head) and possessive-of (the head of the boy). There is compelling evidence that preference for possessive-s is strongly influenced by the possessor's animacy. A corpus analysis that counts each genitive construction in three conditions (definite, indefinite and no article) confirms that occurrences of possessive-s decline as the animacy hierarchy progresses from animate through concrete to abstract. A computer program (Animyser) is developed to obtain results-counts from phrase-searches of Wikipedia that provide multiple genitive ratios for any target noun. Key ratios are identified and algorithms developed, with specific applications achieving classification accuracies of over 80%. The algorithms, based on logistic regression, produce a score of relative animacy that can be applied to individual nouns or to texts. The genitive ratio is a tool with potential applications in any research domain where the relative animacy of language might be significant. Three such applications exemplify that. Combining GR analysis with other factors might enhance established co-reference (anaphora) resolution algorithms. In sentences formed from pairings of animate with concrete or abstract nouns, the animate noun is usually salient, more likely to be the grammatical subject or thematic agent, and to co-refer with a succeeding pronoun or noun-phrase. Two experiments, online sentence production and corpus-based, demonstrate that the GR algorithm reliably predicts the salient noun. Replication of the online experiment in Italian suggests that the GR might be applied to other languages by using English as a 'bridge'. In a mental health context, studies have indicated that Alzheimer's patients' language becomes progressively more concrete; depressed patients' language more abstract. Analysis of sample texts suggests that the GR might monitor the prognosis of both illnesses, facilitating timely clinical interventions

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

Computer-based characterization of language alterations throughout the Alzheimer's disease continuum

According to the American and Canadian Alzheimer’s Associations, research into methods for the early detection of Alzheimer’s disease is imperative. Many studies have emphasized the numerous advantages for patients, family members and governments of detecting the disease at the pre-clinical stage of its continuum. However, at this stage, changes are very subtle, making their detection a challenging task. Alterations in language functions have been found years before the dementia stage of the disease continuum. For this reason, many researchers have focused their efforts on investigating methods for identifying cues of the presence of the disease hidden in language. One type of cognitive test commonly used in this type of research consists of standardized picture description tasks. These tasks elicit the speech of patients through a visual stimulus, and are usually part of cognitive assessment batteries used in clinical practice. The tasks have the advantage of presenting patients with a single constrained thematic, which limits the vocabulary and facilitates comparisons across patients and languages. However, they also limit the variety of syntactic structures, hindering some linguistic analyses, and being a part of usual clinical examinations, may increase nervousness in some patients. The study of spontaneous conversations is an alternative to using picture description tasks for language analyses. Spontaneous conversations have the advantage of allowing the use of unconstrained idiosyncratic syntactic structures and vocabulary. They are also less stressful to patients and could be conducted with a nurse, a caregiver or a person familiar to the patient. Nevertheless, many factors, such as socio-demographic and cultural differences, may define the linguistic characteristics of individuals. Consequently, a characterization of the changes in language functions that occur during the continuum of the disease could be helpful in the monitoring of patient-specific changes. This doctoral thesis presents a computer-based methodology for evaluating patients’ performance during standardized picture description tasks, and for assessing language functions in the context of these tasks and in spontaneous conversations. We believe that both evaluations can complement each other and provide an inexpensive and noninvasive method for monitoring language functions. In practice, picture description tasks could be realized routinely at the doctor’s office, while spontaneous conversations could be held at more regular intervals and at more convenient locations for the patient. For our work, we compared the computed performance and language functions of patients during standardized picture description tasks against a population with similar socio-demographic characteristics. For this, our proposed method evaluated the informativeness and pertinence of the descriptions of patients, as well as their lexical richness. Using our metrics, we trained machine learning algorithms to estimate their adeptness at differentiating Alzheimer’s patients from healthy controls. We obtained an area under the curve of 0.83 in this task. We also achieved an area under the curve of 0.79 for classifying healthy controls and patients with mild cognitive impairment, which is often a pre-clinal precursor of Alzheimer’s disease. In addition, we proposed an automated method for evaluating lexical richness, vocabulary distribution, speech fluidity and the use of specific syntactic structures among older French speakers during spontaneous conversations. We characterized the changes that four speakers underwent as they transitioned from a healthy state to some form of cognitive disease, including Alzheimer’s disease. We observed marked differences in our proposed metrics between those individuals that would develop a cognitive disease and healthy matched controls, even when analyzing transcriptions of conversations from up to ten years before the time of diagnosis. As a concomitant contribution of this doctoral work, we designed the protocol and created the Spanish cohort of the Carolinas’ Conversations Collection. This cohort includes longitudinal video-recordings and transcriptions of spontaneous conversations of older Spanish speakers in Mexico and Ecuador. These recollections are the result of the combined efforts of six institutions from four different countries, and will be available for research purposes upon request. This undertaking is aimed at lessening the scarcity of data of this type, and at encouraging research on language and communication in the older population

Physical and Mental Coordination in the Elderly: A Causal Role for the Cerebellum?

The mechanisms underlying the progressive changes in tissues and organs that characterise normal ageing remain unclear. The cerebellum is known to play a major role in motor function, but recent research suggests it plays an equivalent role in cognition. Working with the hypothesis that cortico-cerebellar loops ensure smooth and coordinated activity in both domains, this thesis investigates the possible role of the cerebellum in normal ageing and in interventions to improve function, seeking to contribute to both theoretical and applied approaches to ageing. Study one investigated relationships between motor and cognitive function using raw data from a national normative sample of adults aged 16 to 75, employing a test battery assessing motor and cognitive skills. Differences between age groups were demonstrated in some tests of complex processing speed, working memory and executive function, with suggestive evidence that senescence in tests is reflected in tests sensitive to cerebellar function. Study two refined the battery, while including further measures of motor and memory performance to investigate linkages between cognitive and cerebellar function. Using a sample of 256 older adults, results were variable but provided evidence that pegboard performance could act as a predictor of some cognitive functions. Study three investigated a proactive intervention for healthy older adults designed to improve cerebellar function, and therefore balance and executive function. This involved an 8-10 week self-administered, internet-based coordinative exercise intervention using a ‘cerebellar challenge’ suite of graded activities. Performance on a basket of tests was assessed before and after, and also compared with performance changes in a no-intervention control group. Significantly greater benefits for the intervention group than the controls were found for balance physical coordination and controlled information processing. Overall, these studies support current research indicating cerebellar contribution to both cognitive and motor problems arising in old age, and present evidence that non-verbal memory and controlled speeded information problems may be alleviated through targeted activities affecting cerebellar function improving postural stability and physical coordination