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1. An Approach Towards the Security Management for Sensitive Medical Data in the IoMT Ecosystem

   https://doi.org/10.1145/3565287.3623388  

   Chatterjee, Pushpita; Das, Debashis; Banerjee, Sourav; Ghosh, Uttam; Mpembele, Armando B; Rogers, Tamara (October 2023, ACM)

   The Internet of Medical Things (IoMT) is a network of interconnected medical devices, wearables, and sensors integrated into healthcare systems. It enables real-time data collection and transmission using smart medical devices with trackers and sensors. IoMT offers various benefits to healthcare, including remote patient monitoring, improved precision, and personalized medicine, enhanced healthcare efficiency, cost savings, and advancements in telemedicine. However, with the increasing adoption of IoMT, securing sensitive medical data becomes crucial due to potential risks such as data privacy breaches, compromised health information integrity, and cybersecurity threats to patient information. It is necessary to consider existing security mechanisms and protocols and identify vulnerabilities. The main objectives of this paper aim to identify specific threats, analyze the effectiveness of security measures, and provide a solution to protect sensitive medical data. In this paper, we propose an innovative approach to enhance security management for sensitive medical data using blockchain technology and smart contracts within the IoMT ecosystem. The proposed system aims to provide a decentralized and tamper-resistant plat- form that ensures data integrity, confidentiality, and controlled access. By integrating blockchain into the IoMT infrastructure, healthcare organizations can significantly enhance the security and privacy of sensitive medical data. 

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2. Personalized Diabetes Management with Digital Twins: A Patient-Centric Knowledge Graph Approach

   https://doi.org/10.3390/jpm14040359  

   Sarani_Rad, Fatemeh; Hendawi, Rasha; Yang, Xinyi; Li, Juan (April 2024, Journal of Personalized Medicine)

   Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and personal health knowledge graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange while ensuring high levels of accuracy in data representation and health insights. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it, enhancing the precision and accuracy of the care provided. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As a proof of concept, we have demonstrated the versatility of our digital twins by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes. 

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3. Improving decision support systems with machine learning: Identifying barriers to adoption

   https://doi.org/10.1002/agj2.21432  

   Brugler, Skye; Gardezi, Maaz; Dadkhah, Ali; Rizzo, Donna_M; Zia, Asim; Clay, Sharon_A (August 2023, Agronomy Journal)

   Abstract Precision agriculture (PA) has been defined as a “management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.” This definition suggests that because PA should simultaneously increase food production and reduce the environmental footprint, the barriers to adoption of PA should be explored. These barriers include (1) the financial constraints associated with adopting decision support system (DSS); (2) the hesitancy of farmers to change from their trusted advisor to a computer program that often behaves as a black box; (3) questions about data ownership and privacy; and (4) the lack of a trained workforce to provide the necessary training to implement DSSs on individual farms. This paper also discusses the lessons learned from successful and unsuccessful efforts to implement DSSs, the importance of communication with end users during DSS development, and potential career opportunities that DSSs are creating in PA. 

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4. Examining the social and biophysical determinants of U.S. Midwestern corn farmers’ adoption of precision agriculture

   https://doi.org/10.1007/s11119-019-09681-7  

   Gardezi, Maaz; Bronson, Kelly (August 2019, Precision Agriculture)

   Precision agricultural technologies (PA) such as global positioning system tools have been commercially available since the early 1990s and they are widely thought to have environmental and economic benefit; however, adoption studies show uneven adoption among farmers in the U.S. and Europe. This study aims to tackle a lingering puzzle regarding why some farmers adopt precision agriculture as an approach to food production and why others do not. The specific objective of this study is to examine the social and biophysical determinants of farmers’ adoption of PA. This paper fills a research gap by including measurements of farmer identity—specifically their own conceptions of their role in the food system—as well as their perceptions of biophysical risks as these relate to the adoption of PA among a large sample of Midwestern U.S. farmers. The study has identified that farmer identity and perceptions of environmental risk do indeed influence PA adoption and that these considerations ought to be incorporated into further studies of PA adoption in other jurisdictions. The findings also appear to highlight the social force of policy and industry efforts to frame PA as not only good for productivity and efficiency but also as an ecologically beneficial technology. 

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5. ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

   https://doi.org/10.3390/s24206601  

   Baniya, Babu Kaji (October 2024, Sensors)

   The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results. 

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