How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease.

Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability

A Comparison of Connected Speech Tasks for Detecting Early Alzheimer's Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning

Alzheimer’s disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited – 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) – and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

Understanding, measuring and treating eating disorders in those with type 1 diabetes

The purpose of this thesis was to explore the nature of Eating Disorders in Type 1 Diabetes. Whether or not Eating Disorders are more prevalent in this demographic is a topic of contention but regardless there is a consensus that those with comorbid Type 1 have considerably worse outcomes and are significantly more difficult to treat. It has been argued that this may be due to a feature unique to this population; insulin omission for weight control. The first aim of this thesis was to systematically review how Eating Disorders have been measured in Type 1 Diabetes, paying particular attention to whether researchers have taken the role of Diabetes regimen and insulin omission into account. Following this a comparison between two Eating Disorder scales, one Diabetes specific the other not, was made in order to compare prevalence rates, to explore which items may be potentially biased and to investigate what the effect of modification may be. The structure of the Diabetes specific scale (the Diabetes Eating Problem Scale Revised) was then explored. The second aim of this thesis was to replicate a pilot study that aimed to explore demographic, psychological and health seeking features of those with Type 1 Diabetes related Eating Disorders. This formed the basis of a structural model whereby psychological and Diabetes specific traits were hypothesised to predict Eating Disorder behaviour and elevated blood glucose levels. A questionnaire built for that study regarding patient attributions was also reanalysed using new data. The final aim was to investigate how Eating Disorders in Type 1 Diabetes have been treated by reviewing literature from the last 2 decades, paying particular attention as to how treatment providers have accommodated the unique needs of those with T1D and whether or not programmes have been successful in relation to both psychological and biological outcomes

Court Review: The Journal of the American Judges Association 50:2 (2014)- Whole Issue

ARTICLES 44 Brain Imaging for Judges: An Introduction to Law and Neuroscience. 52 Evidentiary Incommensurability: A Preliminary Exploration of the Problem of Reasoning from General Scientific Data to Individualized Legal Decision-Making. 62 The Admissibility of Brain Scans in Criminal Trials: The Case of Positron Emission Tomography. 70 Should the Science of Adolescent Brain Development Inform Public Policy?. 78 Pain as Fact and Heuristic: How Pain Neuroimaging Illuminates Moral Dimensions of Law. 94 The Status of NeuroLaw: A Plea for Current Modesty and Future, Cautious Optimism. ESSAY 104 Keeping Up with Neurolaw: What to Know and Where to Look. DEPARTMENTS 42 Editor’s Note. 43 President’s Column. 108 The Resource Page

Understanding, measuring and treating eating disorders in those with type 1 diabetes

The purpose of this thesis was to explore the nature of Eating Disorders in Type 1 Diabetes. Whether or not Eating Disorders are more prevalent in this demographic is a topic of contention but regardless there is a consensus that those with comorbid Type 1 have considerably worse outcomes and are significantly more difficult to treat. It has been argued that this may be due to a feature unique to this population; insulin omission for weight control. The first aim of this thesis was to systematically review how Eating Disorders have been measured in Type 1 Diabetes, paying particular attention to whether researchers have taken the role of Diabetes regimen and insulin omission into account. Following this a comparison between two Eating Disorder scales, one Diabetes specific the other not, was made in order to compare prevalence rates, to explore which items may be potentially biased and to investigate what the effect of modification may be. The structure of the Diabetes specific scale (the Diabetes Eating Problem Scale Revised) was then explored. The second aim of this thesis was to replicate a pilot study that aimed to explore demographic, psychological and health seeking features of those with Type 1 Diabetes related Eating Disorders. This formed the basis of a structural model whereby psychological and Diabetes specific traits were hypothesised to predict Eating Disorder behaviour and elevated blood glucose levels. A questionnaire built for that study regarding patient attributions was also reanalysed using new data. The final aim was to investigate how Eating Disorders in Type 1 Diabetes have been treated by reviewing literature from the last 2 decades, paying particular attention as to how treatment providers have accommodated the unique needs of those with T1D and whether or not programmes have been successful in relation to both psychological and biological outcomes

Case finding for depression in patients with long-term physical conditions in primary care

The aim of this thesis was to describe the impact and consequences of case finding for depression in patients with long-term physical conditions in primary care from the perspective of primary healthcare professionals. Study one (chapter two) evaluated the effects of incentivised case finding using an interrupted time series analysis of routinely collected data. It found that incentivised case finding increased new depression-related diagnoses and rates of antidepressant prescribing. Increased prescribing is of concern as it may include treatment of people unlikely to respond to medication. Study two (chapter three) identified and classified what has been written about primary healthcare professionals beliefs on implementing case finding using a systematic review and the ‘best fit’ framework synthesis approach. A range of contradictory beliefs and three new themes were identified; mistrust, trade-offs and dilemmas. These findings demonstrate conflict and tensions which could undermine implementation of case finding. Study three (chapter four) characterised the range of positions held by primary healthcare professionals on the role, implementation and value of case finding using an online Q method study involving primary healthcare professionals. Three recognisable positions were produced; objections to the principle of case finding for depression, case finding for depression is worthwhile and objections to implementation of case finding for depression. These positions may influence how clinicians deliver and respond to case finding. Implementation is challenging if there is a spread of perspectives. These findings, considered alongside the absence of evidence that case finding improves clinical outcomes, indicate that case finding for depression in long-term physical conditions should not be recommended or incentivised until more robust evidence of improved patient outcomes resulting from the changes case finding is likely to drive, especially in prescribing, and acceptability to professionals becomes available