Detection of Dementia-Related Abnormal Behaviour Using Recursive Auto-Encoders.

Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE's reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to detect early indicators of dementia

Delivering safe, effective nutrition and hydration care to residents with dysphagia: a theory-based approach to developing a link dysphagia practitioner

This report is independent research funded by the National Institute for Health Research (Research for Patient Benefit programme, NIHR200091). The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. No Abstract available

Delivering safe, effective nutrition and hydration care to residents with dysphagia: a theory-based approach to developing a link dysphagia practitioner

This report is independent research funded by the National Institute for Health Research (Research for Patient Benefit programme, NIHR200091). The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. No Abstract available

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces

Longer-Term Psychiatric Inpatient Care for Adolescents

This open access book describes the theoretical underpinnings and operational aspects of delivering longer-term inpatient psychiatric care to adolescents experiencing severe, unremitting mental illness. The authorship is drawn from the multidisciplinary team that supports the Walker Adolescent Unit, located in Sydney, Australia. The book begins with an account of the planning and development of the unit, an examination of the physical environment, and the adaptations that have been made to ensure its functionality. There follows a consideration of the therapeutic milieu. The book describes clinical processes such as admission and discharge planning, formulation and case review. There is information about the specific roles of professionals and the therapies that they provide. The book describes the steps taken to maintain and enhance the physical wellbeing of patients. There are chapters dedicated to governance, and to training and education. The final chapter describes how the unit responded to challenges created by the COVID-19 pandemic

Experienced Carers Helping Others (ECHO): protocol for a pilot randomised controlled trial to examine a psycho-educational intervention for adolescents with anorexia nervosa and their carers

Experienced Carers Helping Others (ECHO) is an intervention for carers of people with eating disorders. This paper describes the theoretical background and protocol of a pilot multicentre randomised controlled trial that will explore the use of two variants of ECHO for improving outcomes for adolescents with anorexia nervosa (AN) referred for outpatient care. Adolescent patients and their carers (typically parents and close others in a supportive role) will be recruited from 38 eating disorder outpatient services across the UK. Carers will be randomly allocated to receive ‘ECHOc’ guided self-help (in addition to treatment as usual), ‘ECHO’ self-help only (in addition to treatment as usual) or treatment as usual only. Primary outcomes are a summary measure of the Short Evaluation of Eating Disorders at 6- and 12-month follow-ups. Secondary outcomes are general psychiatric morbidity of AN patients and carer, carers' coping and behaviour, and change in healthcare use and costs at 6- and 12-month follow-ups. Therapist effects will be examined, and process evaluation of ECHOc will be completed. The findings from this pilot trial will be used in preparation for executing a definitive trial to determine the impact of the preferred variant of ECHO to improve treatment outcomes for AN

Diabetes and artificial intelligence (AI) beyond the closed loop: A review of the landscape, promise and challenges for AI-supported management and self-care for all diabetes types.

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.</p