Developing Intelligent Interviewers to Collect the Medical History: Lessons Learned and Guidelines

Background: Physicians spend a lot of time in routine tasks, i.e. repetitive and time consuming tasks that are essential for the diagnostic and treatment process. One of these tasks is to collect information on the patient's medical history. Objectives: We aim at developing a prototype for an intelligent interviewer that collects the medical history of a patient before the patient-doctor encounter. From this and our previous experiences in developing similar systems, we derive recommendations for developing intelligent interviewers for concrete medical domains and tasks. Methods: The intelligent interviewer was implemented as chatbot using IBM Watson assistant in close cooperation with a family doctor. Results: AnCha is a rule-based chatbot realized as decision tree with 75 nodes. It asks a maximum of 44 questions on the medical history, current complaints and collects additional information on the patient, social details, and prevention. Conclusion: When developing an intelligent digital interviewer it is essential to define its concrete purpose, specify information to be collected, design the user interface, consider data security and conduct a practice-oriented evaluation

Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects

The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications

2ARTs – Decision Support System for Exercise and Diet Prescriptions in Cardiac Recovery Patients

The global health care system is faced with a variety of complicated challenges, ranging from limited access and increasing expenses to an aging population causing increased pressure on healthcare systems. Healthcare professionals are seeking alternative approaches to provide fair access and sustain high-quality care for everyone as a result of these challenges. Patients have historically been restricted from accessing essential healthcare services due to traditional barriers like geographic distance, financial and resource limitations. Innovative solutions to these problems are starting to take shape, thanks to the growth of eHealth platforms that use technology to improve patient care. Through a comprehensive study of existing solutions in the healthcare domain, particularly in cardiology, we identified the need for a Decision Support System (DSS) that would empower physicians with valuable insights and facilitate informed physical and diet prescribing practices into Cardiac Rehabilitation Programmes (CRPs). The major goal of 2ARTs’ project is to create and implement a cardiac rehabilitation platform into a hospital's infrastructure. A key aspect of this platform is the integration of a decision support system designed to provide physicians with valuable information when prescribing individualized treatment prescriptions for each patient, minimizing the potential of human error. The DSS uses algorithms and predictive models to classify patients into distinct groups based on their features and medical history. This classification provides critical insights and additional knowledge to doctors, allowing them to make informed judgments regarding the most effective treatment options for each patient's cardiac rehabilitation journey. By using the power of data-driven analytics and machine learning, the DSS enables doctors to better understand each patient's needs and personalize treatment actions accordingly. In order to achieve the best possible results aligned with the goals of the project, a variety of approaches based on comprehensive studies were explored, specifically feature selection and feature reduction methods, where their performance metrics were evaluated, seeking the most effective solution. It was through this thorough analysis that Principal Component Analysis (PCA) emerged as the standout choice. PCA not only demonstrated superior outcomes in evaluation metrics, but also showcased excellent compatibility with the selected clustering algorithm along with the best results after an expert analysis. Moreover, with the analysis of the data types and features the dataset had, the K-Means algorithm produced the best results and was more adaptable to our dataset. We were able to identify useful insights and patterns within the data by employing both PCA and K-Means, opening the way for more accurate and informed decision-making in the 2ARTs project

Developing of Q&A bots for medicinal disclosure for CKD-patients

This thesis was a subproject of the RealCo project by Prof. Dr. Thomas Keller at ZHAW which provides information about a medication called SGLT2 inhibitors to patients with chronic kidney disease (CKD). While the goal of the project is to improve patient health literacy and compliance, this thesis rather focused on developing a chatbot to answer questions related to the use of SGLT2 inhibitors to CKD patients. Chatbots, as software components that communicate with users via natural language, are considered as an appropriate instrument for improving health literacy. The developed chatbots were implemented using natural language understanding (NLU) platforms, which, due to their structure, enable rapid prototyping, deployment and simple integrations. This thesis addressed the question of which NLU platform is most suitable for the use case. In this thesis, two artefacts were built with over 800 training questions about SGLT2 inhibitors to answer the question above. The developed chatbots were tested with physicians and pharmacists for correctness. The results showed that DialogFlow and Watson Assistant are the most popular and widely used NLU platforms were therefore selected for the chatbot development. The tests conducted and the feedback gathered from physicians and pharmacists showed that the answers were medically correct and the chatbot was perceived as friendly and appealing. Also, in the majority of cases, users received an answer that was relevant to their question. The implementation of the chatbots in these two platforms demonstrated that Watson Assistant was superior to DialogFlow in terms of latency as well as the delivery of the correct answer to the question asked. Future studies using the existing chatbots within the RealCo project should involve patients for testing and further development

Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference

pHealth 2021. Proc. of the 18th Internat. Conf. on Wearable Micro and Nano Technologies for Personalised Health, 8-10 November 2021, Genoa, Italy

Smart mobile systems – microsystems, smart textiles, smart implants, sensor-controlled medical devices – together with related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and the next generation of healthcare services. The multilateral benefits of pHealth technologies offer enormous potential for all stakeholder communities, not only in terms of improvements in medical quality and industrial competitiveness, but also for the management of healthcare costs and, last but not least, the improvement of patient experience. This book presents the proceedings of pHealth 2021, the 18th in a series of conferences on wearable micro and nano technologies for personalized health with personal health management systems, hosted by the University of Genoa, Italy, and held as an online event from 8 – 10 November 2021. The conference focused on digital health ecosystems in the transformation of healthcare towards personalized, participative, preventive, predictive precision medicine (5P medicine). The book contains 46 peer-reviewed papers (1 keynote, 5 invited papers, 33 full papers, and 7 poster papers). Subjects covered include the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, the Health Internet of Things (HIoT), as well as potential risks for security and privacy, and the motivation and empowerment of patients in care processes. Providing an overview of current advances in personalized health and health management, the book will be of interest to all those working in the field of healthcare today

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202