A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia

Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods

Fuzzy adaptive cognitive stimulation therapy generation for Alzheimer’s sufferers: Towards a pervasive dementia care monitoring platform

In this paper, we present a novel system for cognitive stimulation therapy to progressively assess cognitive impairment and emotional well-being of dementia patients in social care settings. The system assesses patients interactions and computes performance scores for different areas of cognitive stimulation. Patient interactions are initially classified into predefined performance categories through clustering of a sampled population. New personalized stimulation plans tailored to match the patient’s changing level of impairment are generated automatically through a set of fuzzy rule based systems using quantitative attributes and the overall scores of patients interactions. Therapists can redefine, evaluate and adjust the rules governing difficulty and activity levels for different stimulation areas to fine tune generated activity plans. The system can also be combined with an Internet of Things (IoT) enabled patient dialogue system for determining the affective state of participants during therapy sessions that could be used as a pervasive condition monitoring platform. Experiments consisting of therapy sessions of patients interacting with the system were performed in which the activity plans were automatically generated. Initial results showed that the system outputs were in agreement with the therapists own assessment in most of the stimulation areas. Simulation experiments were also conducted to analyse the system performance over multiple sessions. The results suggest that the system is able to adapt therapy plans overtime in response to changing levels of impairment/performance while supporting therapists to tune and evaluate therapy plans more effectively

A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients

In this paper, we discuss the development of an ambient intelligent-based system for the monitoring of dementia patients living in their own homes. Within this system groups of unobtrusive wireless sensor devices can be deployed at specific locations within a patient’s home and accessed via standardized interfaces provided through an open middleware platform. For each sensor group intelligent agents are used to learn fuzzy rules, which model the patient’s habitual behaviours in the environment. An online rule adaptation technique is applied to facilitate short-term tuning of the learnt behaviours, and long-term tracking of behaviour changes which could be due to the effects of cognitive decline caused from dementia. The proposed system reports macro level behaviour changes and micro level perception drift to care providers to enable them to make better-informed assessments of the patient’s cognitive abilities and changing care needs. We demonstrate experiments in a real pervasive computing environment, in which our intelligent agent approach can learn to model the user’s behaviours and allow online adaptation of its model to better approximate the learnt behaviours and identify long-term macro-level behaviour changes, which could be attributed to cognitive decline. We also show an example of how the user’s perceptions for thermal comfort may be captured and visualised to provide a means by which micro-level perception changes can be monitored

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living

Human-AI Interaction in the Presence of Ambiguity: From Deliberation-based Labeling to Ambiguity-aware AI

Ambiguity, the quality of being open to more than one interpretation, permeates our lives. It comes in different forms including linguistic and visual ambiguity, arises for various reasons and gives rise to disagreements among human observers that can be hard or impossible to resolve. As artificial intelligence (AI) is increasingly infused into complex domains of human decision making it is crucial that the underlying AI mechanisms also support a notion of ambiguity. Yet, existing AI approaches typically assume that there is a single correct answer for any given input, lacking mechanisms to incorporate diverse human perspectives in various parts of the AI pipeline, including data labeling, model development and user interface design. This dissertation aims to shed light on the question of how humans and AI can be effective partners in the presence of ambiguous problems. To address this question, we begin by studying group deliberation as a tool to detect and analyze ambiguous cases in data labeling. We present three case studies that investigate group deliberation in the context of different labeling tasks, data modalities and types of human labeling expertise. First, we present CrowdDeliberation, an online platform for synchronous group deliberation in novice crowd work, and show how worker deliberation affects resolvability and accuracy in text classification tasks of varying subjectivity. We then translate our findings to the expert domain of medical image classification to demonstrate how imposing additional structure on deliberation arguments can improve the efficiency of the deliberation process without compromising its reliability. Finally, we present CrowdEEG, an online platform for collaborative annotation and deliberation of medical time series data, implementing an asynchronous and highly structured deliberation process. Our findings from an observational study with 36 sleep health professionals help explain how disagreements arise and when they can be resolved through group deliberation. Beyond investigating group deliberation within data labeling, we also demonstrate how the resulting deliberation data can be used to support both human and artificial intelligence. To this end, we first present results from a controlled experiment with ten medical generalists, suggesting that reading deliberation data from medical specialists significantly improves generalists' comprehension and diagnostic accuracy on difficult patient cases. Second, we leverage deliberation data to simulate and investigate AI assistants that not only highlight ambiguous cases, but also explain the underlying sources of ambiguity to end users in human-interpretable terms. We provide evidence suggesting that this form of ambiguity-aware AI can help end users to triage and trust AI-provided data classifications. We conclude by outlining the main contributions of this dissertation and directions for future research

Developing artificial intelligence and machine learning to support primary care research and practice

This thesis was motivated by the potential to use everyday data , especially that collected in electronic health records (EHRs) as part of healthcare delivery, to improve primary care for clients facing complex clinical and/or social situations. Artificial intelligence (AI) techniques can identify patterns or make predictions with these data, producing information to learn about and inform care delivery. Our first objective was to understand and critique the body of literature on AI and primary care. This was achieved through a scoping review wherein we found the field was at an early stage of maturity, primarily focused on clinical decision support for chronic conditions in high-income countries, with low levels of primary care involvement and model evaluation in real-world settings. Our second objective was to demonstrate how AI methods can be applied to problems in descriptive epidemiology. To achieve this, we collaborated with the Alliance for Healthier Communities, which provides team-based primary health care through Community Health Centres (CHCs) across Ontario to clients who experience barriers to regular care. We described sociodemographic, clinical, and healthcare use characteristics of their adult primary care population using EHR data from 2009-2019. We used both simple statistical and unsupervised learning techniques, applied with an epidemiological lens. In addition to substantive findings, we identified potential avenues for future learning initiatives, including the development of decision support tools, and methodological considerations therein. Our third objective was to advance interpretable AI methodology that is well-suited for heterogeneous data, and is applicable in clinical epidemiology as well as other settings. To achieve this, we developed a new hybrid feature- and similarity-based model for supervised learning. There are two versions, fit by convex optimization with a sparsity-inducing penalty on the kernel (similarity) portion of the model. We compared our hybrid models with solely feature- and similarity-based approaches using synthetic data and using CHC data to predict future loneliness or social isolation. We also proposed a new strategy for kernel construction with indicator-coded data. Altogether, this thesis progressed AI for primary care in general and for a particular health care organization, while making research contributions to epidemiology and to computer science

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

An affective computing and image retrieval approach to support diversified and emotion-aware reminiscence therapy sessions

A demência é uma das principais causas de dependência e incapacidade entre as pessoas idosas em todo o mundo. A terapia de reminiscência é uma terapia não farmacológica comummente utilizada nos cuidados com demência devido ao seu valor terapêutico para as pessoas com demência. Esta terapia é útil para criar uma comunicação envolvente entre pessoas com demência e o resto do mundo, utilizando as capacidades preservadas da memória a longo prazo, em vez de enfatizar as limitações existentes por forma a aliviar a experiência de fracasso e isolamento social. As soluções tecnológicas de assistência existentes melhoram a terapia de reminiscência ao proporcionar uma experiência mais envolvente para todos os participantes (pessoas com demência, familiares e clínicos), mas não estão livres de lacunas: a) os dados multimédia utilizados permanecem inalterados ao longo das sessões, e há uma falta de personalização para cada pessoa com demência; b) não têm em conta as emoções transmitidas pelos dados multimédia utilizados nem as reacções emocionais da pessoa com demência aos dados multimédia apresentados; c) a perspectiva dos cuidadores ainda não foi totalmente tida em consideração. Para superar estes desafios, seguimos uma abordagem de concepção centrada no utilizador através de inquéritos mundiais, entrevistas de seguimento, e grupos de discussão com cuidadores formais e informais para informar a concepção de soluções tecnológicas no âmbito dos cuidados de demência. Para cumprir com os requisitos identificados, propomos novos métodos que facilitam a inclusão de emoções no loop durante a terapia de reminiscência para personalizar e diversificar o conteúdo das sessões ao longo do tempo. As contribuições desta tese incluem: a) um conjunto de requisitos funcionais validados recolhidos com os cuidadores formais e informais, os resultados esperados com o cumprimento de cada requisito, e um modelo de arquitectura para o desenvolvimento de soluções tecnológicas de assistência para cuidados de demência; b) uma abordagem end-to-end para identificar automaticamente múltiplas informações emocionais transmitidas por imagens; c) uma abordagem para reduzir a quantidade de imagens que precisam ser anotadas pelas pessoas sem comprometer o desempenho dos modelos de reconhecimento; d) uma técnica de fusão tardia interpretável que combina dinamicamente múltiplos sistemas de recuperação de imagens com base em conteúdo para procurar eficazmente por imagens semelhantes para diversificar e personalizar o conjunto de imagens disponíveis para serem utilizadas nas sessões.Dementia is one of the major causes of dependency and disability among elderly subjects worldwide. Reminiscence therapy is an inexpensive non-pharmacological therapy commonly used within dementia care due to its therapeutic value for people with dementia. This therapy is useful to create engaging communication between people with dementia and the rest of the world by using the preserved abilities of long-term memory rather than emphasizing the existing impairments to alleviate the experience of failure and social isolation. Current assistive technological solutions improve reminiscence therapy by providing a more lively and engaging experience to all participants (people with dementia, family members, and clinicians), but they are not free of drawbacks: a) the multimedia data used remains unchanged throughout sessions, and there is a lack of customization for each person with dementia; b) they do not take into account the emotions conveyed by the multimedia data used nor the person with dementia’s emotional reactions to the multimedia presented; c) the caregivers’ perspective have not been fully taken into account yet. To overcome these challenges, we followed a usercentered design approach through worldwide surveys, follow-up interviews, and focus groups with formal and informal caregivers to inform the design of technological solutions within dementia care. To fulfil the requirements identified, we propose novel methods that facilitate the inclusion of emotions in the loop during reminiscence therapy to personalize and diversify the content of the sessions over time. Contributions from this thesis include: a) a set of validated functional requirements gathered from formal and informal caregivers, the expected outcomes with the fulfillment of each requirement, and an architecture’s template for the development of assistive technology solutions for dementia care; b) an end-to-end approach to automatically identify multiple emotional information conveyed by images; c) an approach to reduce the amount of images that need to be annotated by humans without compromising the recognition models’ performance; d) an interpretable late-fusion technique that dynamically combines multiple content-based image retrieval systems to effectively search for similar images to diversify and personalize the pool of images available to be used in sessions

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications