This book delves into emerging fields of science and technology, where cognitive computing techniques offer effective solutions that are poised to have a significant impact in the foreseeable future. The fusion of semantic intelligence, machine learning, big data, artificial intelligence, analytics, and natural language processing necessitates collaboration among diverse disciplines, encompassing science, technology, business, and the humanities. This interdisciplinary approach is crucial for the collection and comprehension of information, using various senses and learning from experience. The proliferation of these technologies also presents numerous opportunities to enhance efficiency in healthcare. In this transformative era, errors will be minimised, data insights quicker and more sophisticated, and the system will be capable of proposing personalised care models.
The objective of this book is to assess the current landscape, and identify the roles and challenges encountered in the integration of cognitive computing techniques into the widespread adoption of innovative smart healthcare solutions. The healthcare domain is characterised by an array of data types and formats, including drug dosages, molecular structures, text, numerical data, and images, and healthcare systems are continuously acquiring novel data from cutting-edge technologies such as wearable devices. Semantic intelligence technologies are taking centre stage in the development of intelligent, knowledge-based systems, as they enable machines to intelligently integrate and process resources within relevant contexts. This includes a range of semantic intelligence technologies such as artificial intelligence, machine learning, deep learning, the internet of things, and the hybrid methodologies combining these approaches.
The book’s content spans a wide spectrum of cutting-edge topics and emerging trends, reflecting the multidisciplinary nature of healthcare and cognitive computing, from digital twins to eXplainable AI, from AI-based decision support systems in intensive care to semantics for culinary health care and the semantic internet of things, and from natural language processing to deep learning and graph models.
Each chapter is the result of collaboration by experts from various domains and provides a detailed overview of the potential that new technologies in the field of healthcare currently offer or will offer in the near future. We believe that the book will inspire new ideas and facilitate collaboration among the different disciplines involved. The contributions of the individual chapters are as follows.
The first chapter, “Digital twins, digital triplets, and eXplainable AI, in precision health”, explores precision health, focusing on the principles of preventing and treating diseases using digital twins and digital triplets. Digital twins are digital representations of physical objects which facilitate machine learning and knowledge mining, enabling in-silico simulations of health and molecular states and aiding evidence-based medicine. In contrast, digital triplets provide semantic intelligence by clustering similar objects to uncover hidden knowledge and interrelationships, facilitating accurate clinical and medical predictions. The chapter categorises digital twins into person phenotype digital twins, person genotype digital twins, and physicians’ brain digital twins, each of which serve a distinct purposes in advancing precision health.
The second chapter, “AI-based decision support systems in intensive care” delves into the pivotal role of intensive care units (ICUs) in modern healthcare, highlighting the challenges they face, which include high workload, complex decision-making, and managing vast amounts of healthcare data while also considering the individual needs of patients. It explores how AI-based decision support systems (DSS) are advancing healthcare by analysing patient data to make personalised care recommendations. Various AI models, such as regression, decision trees, and neural networks, are discussed. However, the chapter underscores the need for careful evaluation, ethical considerations, and clarification as regards responsibility and accountability for AI-DSS in clinical practice. While AI-DSS has potential, it also faces hurdles like data quality and scepticism among healthcare personnel. Standardisation and regulatory frameworks are deemed crucial for widespread AI adoption in ICUs.
The third chapter, “Semantics for culinary health care” explores the underutilised potential of semantic-web technologies in computational creativity, specifically in culinary healthcare. It discusses the idea of automating recipe generation by extracting and annotating semantic information from existing recipes, representing them as knowledge graphlets. The challenge lies in replicating the complexity of human culinary expertise. The chapter highlights the gap in leveraging the semantic web for creative recipe generation and emphasises the importance of structured approaches. By using semantic information, such as ontologies and knowledge graphs, the chapter envisions the creation of diverse, health-conscious recipes with innovative combinations. It suggests that combining the semantic web with AI can revolutionise the culinary landscape, making it more accessible, innovative, and health-conscious. The chapter is organised to explore the relationship between food and health, automated recipe generation, the role of the semantic web, and concludes with key findings.
The fourth chapter, entitled “Exploring the determinants of the semantic internet of things in healthcare” introduces the concept of the semantic internet of things (SIoT), an extension of the internet of things (IoT), which leverages semantic technologies to enhance interoperability and data understanding among connected devices. The SIoT is particularly valuable in the context of smart health, where it establishes a common language and framework for sharing and integrating health-related data from diverse sources. By doing so, it can enhance the accuracy and efficiency of health monitoring and decision making, enabling more personalised and effective healthcare interventions. The chapter provides an overview of SIoT’s core concepts, outlines its focus areas, and discusses its various requirements, challenges, and applications. It includes a literature review of healthcare systems employing SIoT, highlighting cases of its successful use and challenges. Additionally, the chapter explores the utilisation of semantic technologies in healthcare IoT, discussing new projects, frameworks, and specific ontologies designed to address issues related to semantic interoperability and data heterogeneity. This comprehensive overview serves as a valuable resource and guideline for future research in this field.
The fifth chapter, “Natural language processing for ontology development in IOT-enabled smart healthcare” delves into the challenges of managing vast volumes of unstructured narrative data in the healthcare industry. It highlights how ontologies, which provide formal specifications for health data, enable machine-readable representations and facilitate the sharing and reuse of health information. With the growth of interconnected systems and the internet of things (IoT), healthcare services have evolved, and semantic web technologies and ontologies play a crucial role in ensuring interoperability within IoT ecosystems. The chapter emphasises the importance of natural language processing (NLP) in ontology development for healthcare, as it helps convert unstructured health data into meaningful representations. NLP techniques are essential for cognitive computing systems, enhancing the quality of healthcare services while reducing costs. The chapter explores NLP methods in the context of IoT-enabled healthcare, presenting principles, methodologies for ontology creation using NLP, and the relationship between NLP and the semantic web. It also examines recent studies, applications, opportunities, and challenges in this field.
The sixth chapter is entitled “On the modelling of academic profiles: a semantic-web case of study”, and focuses on enhancing web search results by incorporating semantic-web elements such as ontologies and annotations. It introduces an ontology for academic profiles, and outlines the development of a software system for querying academic profile information. The system comprises a mobile application for Android and a web service for knowledge base access. A case study involving academic profiles at the Faculty of Engineering and Sciences of the Autonomous University of Tamaulipas is also presented. This work aims to improve information retrieval by utilising semantic technologies in academia.
The seventh chapter, “Deep learning to explore the correlation between screen time and depression through analysis of the NHANES dataset” explores the impact, particularly among teenagers, of increased screen time due to factors like online education and entertainment. Excessive screen time, including the use of smart phones, laptops, and televisions, can lead to depressive symptoms, cognitive behaviour issues, loneliness, personality degradation, and even suicidal thoughts in teenagers. The chapter investigates the feasibility of detecting depression based on screen usage and highlights the importance of promoting healthier digital habits. The study uses the NHANES dataset, which includes standardised questionnaires like the Hamilton Rating Scale for Depression (HAM-D), to assess depressive symptoms. The chapter focuses on the HAM-D scale and employs a generalised regression neural network (GRNN) depression-detection model based on screen time. This model achieves an impressive accuracy rate of 98.55%.
The eighth chapter, “Application of factor graph model in stress detection using social network messages” explores the use of social network data to proactively detect psychological stress. With the increasing use of social platforms, users regularly share their daily activities and engage with friends online. The research collects real-time data to investigate the correlation between users’ stress levels and their social interactions. It employs a comprehensive set of stress-related attributes, including textual, visual, and social aspects, to gauge stress levels in users. A novel model is proposed that considers the stress states of both users and their friends, improving detection performance. An accessible website is created to allow users to assess their stress levels and access stress management resources. The model’s effectiveness in detecting stress is demonstrated through experiments, and proactive care and support is offered for individuals facing psychological stress.