Open Knowledge Accessing Method in IoT-based Hospital Information System for Medical Record Enrichment

For a medical treatment with IoT-based facilities, physicians always have to pay much more attentions to the raw medical records of target patients instead of directly making medical advice, conclusions or diagnosis from their experiences. Because the medical records in IoT-based Hospital Information System (HIS) are dispersedly obtained from distributed devices such as tablet computer, personal digital assistant, automated analyzer and other medical devices, they are raw, simple, weak-content and massive. Such medical records cannot be used for further analyzing and decision supporting due to that they are collected in a weak-semantic manner. In this paper, we propose a novel approach to enrich IoT-based medical records by linking them with the knowledge in Linked Open Data (LOD). A case study is conducted on a real-world IoT-based HIS system in association with our approach, the experimental results show that medical records in the local HIS system are significantly enriched and useful for healthcare analysis and decision making, and further demonstrate the feasibility and effectiveness of our approach for knowledge accessing

RANCANG BANGUN ALAT PENGUKURAN FISIK MANUSIA UNTUK DATA MEDIS BERBASIS INTERNET Of THINGS

Technological developments that have begun the 4.0 industrial revolution have made many sectors in everyday life begin to innovate in order to be able to compete and be more efficient. It also penetrated the medical world, which was previously still done manually and requires considerable human resources. However, along with the many innovations that exist in the process that can be automated, ranging from the process of patient registration to examination of a person himself. In this study, the authors designed a tool that can retrieve data from humans for medical needs based on the Internet of Things. Data will be taken from these measurements between the body height of the patient, the body temperature of a patient and the body of a patient at the same time automatically. After data retrieval is carried out by several sensors, then the data will be sent to the broker / server which will be sent to the Node-red dashboard using the MQTT protocol and the data will be transferred to the database. A patient's disease date will be present. ESP32, Ultrasonic Sensor, MLX90614, LoadCell and MYSQL database

A Blockchain based system for Healthcare Digital Twin

Digital Twin (DT) is an emerging technology that replicates any physical phenomenon from a physical space to a digital space in congruence with the physical state. However, devising a Healthcare DT model for patient care is seen as a challenging task as the lack of adequate data collection structure. There are also security and privacy concerns as healthcare data is very sensitive and can be used in malicious ways. Because of these current research gaps, the proper way of acquiring the structured data and managing them in a secure way is very important. In this article, we present a mathematical data model to accumulate the patient relevant data in a structured and predefined way with proper delineation. Additionally, the provided data model is described in harmony with real life contexts. Then, we have used the patient centric mathematical data model to formally define the semantic and scope of our proposed Healthcare Digital Twin ( HDT ) system based on Blockchain. Accordingly, the proposed system is described with all the key components as well as with detailed protocol flows and an analysis of its different aspects. Finally, the feasibility of the proposed model with a critical comparison with other relevant research works have been provided

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic