A Collective Intelligence Framework for Lifestyle Management pro Mental Health Systems

Health Information Management Systems are becoming a central fixture in healthcare settings, but only a few frameworks exist to provide guidelines for the development of an innovative and sustainable system. This study employs a collective intelligence approach by corroborating knowledge, skills and contributions of various stakeholders to develop a Framework for Lifestyle Management pro Mental Health Management Systems (FLMMHS). A mixed-methods approach was employed and covered in two principal phases namely; document analysis (analysis of existing facts about mental health in the body of knowledge) and empirical analysis (experts’ validation using four core parameters namely; efficacy, effectiveness, simplicity and flexibility). FLMMHS’ components are apportioned into three core layers namely; Research Design Evaluation (RDE wrapper), Guidelines and Requirements (G&R), and Diagnosis Prevention Alleviation (DPA). While these components are flexibly designed to allow seamless system integration, its comprehensive representation serves as an implementation platform for the development of mental health systems. Although the suitability of FLMMHS for system development is based on the premise of lifestyle management for mental health, successful evaluation following qualitative and quantitative measures by expert judges impresses its aptness for the development mental health management systems

Cancer support & advice (CANadvice) m-health system for home monitoring and symptom management of patients receiving oral chemotherapy treatment

Introduction: Recent development in digital technology has raised the interest of many researchers in implementing technology in the domain of m-health to provide better service to patients. Capecitabine, an oral chemotherapeutic agent causes several side effects which need to be monitored to avoid severe health consequences in patients. Methodology: A multidisciplinary team of pharmacists, clinicians and developers, was assembled for the design and development of CANadvice system, a mobile android application for patients and a web interface for health care professionals to assist patients in monitoring chemotherapy side effects at home. The application provides real time medical or lifestyle advice to patients and sends alerts to health care professionals for intervention. Results and Discussion: Two prototypes were designed, implemented and evaluated. Interviews were carried out with 5 health care professionals to investigate the system's effectiveness. The results indicated in this paper recommend using such tool for monitoring side effects. Real time advice and the link to health care professionals were very well accepted

Feature selection for UK disabled students’ engagement post higher education: a machine learning approach for a predictive employment model

While only 4.2 million people out of a population of 7.9 million disabled people are working, a considerable contribution is still required from universities and industries to increase employability among the disabled, in particular, by providing adequate career guidance post higher education. This study aims to identify the potential predictive features, which will improve the chances of engaging disabled school leavers in employment about 6 months after graduation. MALSEND is an analytical platform that consists of information about UK Destinations Leavers from Higher Education (DLHE) survey results from 2012 to 2017. The dataset of 270,934 student records with a known disability provides anonymised information about students’ age range, year of study, disability type, results of the first degree, among others. Using both qualitative and quantitative approaches, characteristics of disabled candidates during and after school years were investigated to identify their engagement patterns. This article builds on constructing and selecting subsets of features useful to build a good predictor regarding the engagement of disabled students 6 months after graduation using the big data approach with machine learning principles. Features such as age, institution, disability type, among others were found to be essential predictors of the proposed employment model. A pilot was developed, which shows that the Decision Tree Classifier and Logistic Regression models provided the best results for predicting the Standard Occupation Classification (SOC) of a disabled school leaver in the UK with an accuracy of 96%

Using machine learning advances to unravel patterns in subject areas and performances of university students with special educational needs and disabilities (MALSEND): a conceptual approach

Universities and colleges in the UK welcome almost 30,000 disabled students each year. Re-search shows that the dropout from education in the EU for the disabled is at 31.5%, much higher compared to only 12.3% for non-disabled students. Supporting young students who require special educational needs in pursuing higher education is an ambitious and necessary step that needs to be adopted by tertiary education providers worldwide. We propose, MALSEND, a project aiming to develop a platform based on machine and human intelligence to understand learning disability patterns in Higher Education. The platform will analyse da-tasets from universities in the previous years and will help to discover any trends in subject areas and performance among autistic students, dyslexic students or students having attention deficit hyperactive disorder (ADHD), among others. Analysing variables such as students’ courses, modules, performances and other engagement-indices will give new insights on re-search questions, career advice and institutional policy making. This paper describes the activ-ities of the development phases of this concept

Features of mobile apps for people with autism in a post covid-19 scenario: current status and recommendations for apps using AI

The new ‘normal’ defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses

Features of a mobile support app for patients with Chronic Obstructive Pulmonary Disease : literature review and current applications

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a serious long-term lung disease in which the airflow from the lungs is progressively reduced. By 2030, COPD will become the third cause of mortality and seventh cause of morbidity worldwide. With advances in technology and mobile communications, significant progress in the mobile health (mHealth) sector has been recently observed. Mobile phones with app capabilities (smartphones) are now considered as potential media for the self-management of certain types of diseases such as asthma, cancer, COPD, or cardiovascular diseases. While many mobile apps for patients with COPD are currently found on the market, there is little published material on the effectiveness of most of them, their features, and their adoption in health care settings. OBJECTIVES: The aim of this study was to search the literature for current systems related to COPD and identify any missing links and studies that were carried out to evaluate the effectiveness of COPD mobile apps. In addition, we reviewed existing mHealth apps from different stores in order to identify features that can be considered in the initial design of a COPD support tool to improve health care services and patient outcomes. METHODS: In total, 206 articles related to COPD management systems were identified from different databases. Irrelevant materials and duplicates were excluded. Of those, 38 articles were reviewed to extract important features. We identified 214 apps from online stores. Following exclusion of irrelevant apps, 48 were selected and 20 of them were downloaded to review some of their common features. RESULTS: Our review found that out of the 20 apps downloaded, 13 (65%, 13/20) had an education section, 5 (25%, 5/20) consisted of medication and guidelines, 6 (30%, 6/20) included a calendar or diary and other features such as reminders or symptom tracking. There was little published material on the effectiveness of the identified COPD apps. Features such as (1) a social networking tool; (2) personalized education; (3) feedback; (4) e-coaching; and (5) psychological motivation to enhance behavioral change were found to be missing in many of the downloaded apps. CONCLUSIONS: This paper summarizes the features of a COPD patient-support mobile app that can be take

Design of a RESTful middleware to enable a web of medical things

In this paper, we consider the design methodology of a mobile patient hub for the remote self-management of COPD patients. The patient hub design forms a part of the WELCOME system. WELCOME is a current EU project that aims to design and develop a new mobile health system to provide integrated care for COPD patients with comorbidities. The approach adopted for this research is based on the Web of Things architecture with RESTful principles as the enabler of communications. The proposed patient hub architecture design is based on three layers: an application layer, a middleware layer and the sensors layer. This paper presents the detail of the initial design of the middleware and an analysis of the architecture in the context of the system's requirements

WELCOME project: What do stakeholders want? In depth analysis of COPD patients, carers and healthcare professional views

Chronic obstructive pulmonary disease is a growing health concern worldwide. Telehealth can facilitate integrated COPD care by providing an opportunity for remote monitoring, early diagnosis and clinical intervention. A design for a telehealth-based system called Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Co-morbidities (WELCOME) has been proposed. This study identifies patients', informal carers' and HCPs' acceptance of and requirements for the WELCOME system in four European countries: the United Kingdom (UK), Ireland, Greece and Netherlands