5 research outputs found
Development of Conversational Artificial Intelligence for Pandemic Healthcare Query Support
The paper proposes and describes the development of conversational artificial intelligence (AI) agent to support hospital healthcare and COVID-19 queries. The conversational AI agent is called “Akira” and it is developed using deep neural network and natural language processing. It is capable of reading the inputs from the user, understanding the input and identifying the intention, and outputting messages towards the user, and these steps are iterated until the user prompts to exit or the programme is terminated. A deep learning model has been trained, and Akira could converse with the user ranging from the conversation over 7 topics related to COVID-19, common cold and flu, mental health, sexual health, abortions, allergens, drugs and medicine. The paper also describes the importance of designing an interactive human-user interface when dealing with conversational agent. In addition. the context of ethical issues and security concerns when designing the agent has been taken into consideration and discussed. The conversational agent is demonstrated to answer queries from a pool of 57 participants
mHealth Engineering: A Technology Review
In this paper, we review the technological bases of mobile health (mHealth). First, we derive a component-based mHealth architecture prototype from an Institute of Electrical and Electronics Engineers (IEEE)-based multistage research and filter process. Second, we analyze medical databases with regard to these prototypic mhealth system components.. We show the current state of research literature concerning portable devices with standard and additional equipment, data transmission technology, interface, operating systems and software embedment, internal and external memory, and power-supply issues. We also focus on synergy effects by combining different mHealth technologies (e.g., BT-LE combined with RFID link technology). Finally, we also make suggestions for future improvements in mHealth technology (e.g., data-protection issues, energy supply, data processing and storage)
CISDA Development Process for decision aids to support self-care decision making
The self-care management of chronic disease patients is complicated by various everyday decisions that range from routine ill-structured problems, e.g., “What to eat?” to uncertain symptoms-related decisions, e.g., “Why do I feel tired?” Such decisions can have significant consequences on a patient’s health, treatment, care, and associated medical costs. Due to the complexity involved in understanding and analysing everyday decision making, there is a lack of empirical research to guide the development of self-care decision aids. This thesis aims to address this problem by formulating and illustrating the Critical Illness Self-care Decision Aid (CISDA) process through a coherent, structured, integrated design and development process using a case study. Following a literature review, the problems in current approaches and the criteria needed for the development were derived from evidence-based frameworks such as chronic disease management, decision aids standards and complex interventions development process for future designs. Mixed methods were used including: focus groups, interviews, questionnaire, Cognitive Work Analysis and case scenarios for not only constructing an account of self-care needs and decisions but also to evaluate the development process and the decision support provided involving patients, doctors, caregivers, non-medical experts like psychologists and IT/Systems engineers. The CISDA process consists of: (i) needs assessment, (ii) theory formation, (iii) modelling, (iv) integration, (v) interface design and development, and (vi) evaluation for addressing the relevant intersection of human factors, systems engineering, and software engineering. This thesis should prove useful to not only systems engineers but also to a range of practitioners concerned about decision making, maintaining a user's cognitive perspective during specification and analysis of a complex system
A quality-of-data aware mobile decision support system for patients with chronic illnesses
We present a mobile decision support system (mDSS) which runs on a patient Body Area Network consisting of a smartphone and a set of biosensors. Quality-of-Data (QoD) awareness in decision making is achieved by means of a component known as the Quality-of-Data Broker, which also runs on the smartphone. The QoD-aware mDSS collaborates with a more sophisticated decision support system running on a fixed back-end server in order to provide distributed decision support. This distributed decision support system has been implemented as part of a larger system developed during the European project MobiGuide. The MobiGuide system is a guideline-based Patient Guidance System designed to assist patients in the management of chronic illnesses. The system, including the QOD-aware mDSS, has been validated by clinicians and is being evaluated in patient pilots against two clinical guidelines