Design of an ontology for decision support in VR exposure therapy

Virtual Reality (VR) is finding its way into many domains, including healthcare. Therapists greatly benefit from having any scenario in VR at their disposal for exposure therapy. However, adapting the VR environment to the needs of the patient is time-consuming. Therefore, an intelligent decision support system that takes context information into account would be a big improvement for personalised VR therapy. In this paper, a semantic ontology is presented for modelling relevant concepts and relations in the context of anxiety therapy in VR. The necessary knowledge was collected through workshops with therapists, this resulted in a layered ontology. Furthermore, semantic reasoning through logical rules enables deduction of interesting high-level knowledge from low-level data. The presented ontology is a starting point for further research on intelligent adaptation algorithms for personalised VR exposure therapy

Adapting structuration theory to understand the role of reflexivity: Problematization, clinical audit and information systems

This paper is an exploratory account of the further development and application of a hybrid framework, StructurANTion, that is based on Structuration Theory and Actor Network Theory (ANT). The use of social theories in general and their use in information systems (IS) research in particular is explored leading to the use of the framework to examine the concept of what are termed humanchine networks in the context of clinical audit, within a healthcare Primary Care Trust (PCT). A particular focus is on the manner in which information systems-based reflexivity contributes to both entrenching a networks’ structurated order as well as contributing to its emancipatory change. The case study compares clinic-centric and patientcentric audit and seeks to further extend the understanding of the role of information and information systems within structurated humanchine activity systems. Conclusions indicate that the use of more socially informed IS methods and approaches can incorporate more emancipatory ideals and lead to greater adoption and usage of more relevant and useful clinical information systems and practices

CoachAI: A Conversational Agent Assisted Health Coaching Platform

Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent assisted health coaching system to support health intervention delivery to individuals and groups. CoachAI instantiates a text based healthcare chatbot system that bridges the remote human coach and the users. This research provides three main contributions to the preventive healthcare and healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain independent mobile health conversational agent for health intervention delivery. We will discuss our approach and analyze the results of a one month validation study on physical activity, healthy diet and stress management

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Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

A model for continuous monitoring of patients with major depression in short and long term periods

The final publication is available at IOS Press through http://dx.doi.org/10.3233/THC-161289BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of disease burden by 2030. The prevalence of depression, all of its social and personal costs, and its recurrent characteristics, put heavy constraints on the ability of the public healthcare system to provide sufficient support for patients with depression. In this research, a model for continuous monitoring and tracking of depression in both short-term and long-term periods is presented. This model is based on a new qualitative reasoning approach. METHOD: This paper describes the patient assessment unit of a major depression monitoring system that has three modules: a patient progress module, based on a qualitative reasoning model; an analysis module, based on expert knowledge and a rules-based system; and the communication module. These modules base their reasoning mainly on data of the patient's mood and life events that are obtained from the patient's responses to specific questionnaires (PHQ-9, M.I.N.I. and Brugha). The patient assessment unit provides synthetic and useful information for both patients and physicians, keeps them informed of the progress of patients, and alerts them in the case of necessity. RESULTS: A set of hypothetical patients has been defined based on clinically possible cases in order to perform a complete scenario evaluation. The results that have been verified by psychiatrists suggest the utility of the platform. CONCLUSION: The proposed major depression monitoring system takes advantage of current technologies and facilitates more frequent follow-up of the progress of patients during their home stay after being diagnosed with depression by a psychiatrist.Peer ReviewedPostprint (author's final draft