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1. Blockchain Framework for Secured On-Demand Patient Health Records Sharing

   Abouali, Meryem; Sharma, Kartikeya; Ajayi, Oluwaseyi; Saadawi, Tarek (December 2021, IEEE UEMCON2021)

   The healthcare sector is constantly improving patient health record systems. However, these systems face a significant challenge when confronted with patient health record (PHR) data due to its sensitivity. In addition, patient’s data is stored and spread generally across various healthcare facilities and among providers. This arrangement of distributed data becomes problematic whenever patients want to access their health records and then share them with their care provider, which yields a lack of interoperability among various healthcare systems. Moreover, most patient health record systems adopt a centralized management structure and deploy PHRs to the cloud, which raises privacy concerns when sharing patient information over a network. Therefore, it is vital to design a framework that considers patient privacy and data security when sharing sensitive information with healthcare facilities and providers. This paper proposes a blockchain framework for secured patient health records sharing that allows patients to have full access and control over their health records. With this novel approach, our framework applies the Ethereum blockchain smart contracts, the Inter-Planetary File System (IPFS) as an off-chain storage system, and the NuCypher protocol, which functions as key management and blockchain-based proxy re-encryption to create a secured on-demand patient health records sharing system effectively. Results show that the proposed framework is more secure than other schemes, and the PHRs will not be accessible to unauthorized providers or users. In addition, all encrypted data will only be accessible to and readable by verified entities set by the patient. 

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2. PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber–Physical Systems

   https://doi.org/10.3390/s24030938  

   Bathalapalli, Venkata K.; Mohanty, Saraju P.; Kougianos, Elias; Iyer, Vasanth; Rout, Bibhudutta (February 2024, Sensors)

   This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber–physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient’s Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle’s Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient’s gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient’s BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time. 

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3. Model-driven engineering for digital twins: a graph model-based patient simulation application

   https://doi.org/10.3389/fphys.2024.1424931  

   Trevena, William; Zhong, Xiang; Lal, Amos; Rovati, Lucrezia; Cubro, Edin; Dong, Yue; Schulte, Phillip; Gajic, Ognjen (August 2024, Frontiers in Physiology)

   IntroductionDigital twins of patients are virtual models that can create a digital patient replica to test clinical interventionsin silicowithout exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. MethodsThis article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. ResultsA short case study is presented to demonstrate the viability of the proposed simulation architecture. DiscussionThe proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians’ bedside decision-making. 

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4. Tackling Bias, Privacy, and Scarcity in Health Data Analytics

   Wang, Shuwen (October 2023, Florida Atlantic University Dissertation)

   Health data analytics has emerged as a critical domain with immense potential to revolutionize healthcare delivery, disease management, and medical research. However, it is confronted by formidable challenges, including sample bias, data privacy concerns, and the cost and scarcity of labeled data. These challenges collectively impede the development of accurate and robust machine learning models for various healthcare applications, from disease diagnosis to treatment recommendations. Sample bias and specificity refer to the inherent challenges in working with health datasets that may not be representative of the broader population or may exhibit disparities in their distributions. These biases can significantly impact the generalizability and effectiveness of machine learning models in healthcare, potentially leading to suboptimal outcomes for certain patient groups. Data privacy and locality are paramount concerns in the era of digital health records and wearable devices. The need to protect sensitive patient information while still extracting valuable insights from these data sources poses a delicate balancing act. Moreover, the geographic and jurisdictional differences in data regulations further complicate the use of health data in a global context. Label cost and scarcity pertain to the often labor-intensive and expensive process of obtaining ground-truth labels for supervised learning tasks in healthcare. The limited availability of labeled data can hinder the development and deployment of machine learning models, particularly in specialized medical domains. This dissertation mainly focuses on health data analytics and explores approaches to tackle the above challenges. More specifically, the following three problems will be studied from different perspectives: (1) Sample bias and specificity in health data. (2) Data privacy and locality in health data. (3) Label cost and scarcity in health data. 

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5. Accessing Artificial Intelligence for Clinical Decision-Making

   https://doi.org/10.3389/fdgth.2021.645232  

   Giordano, Chris; Brennan, Meghan; Mohamed, Basma; Rashidi, Parisa; Modave, François; Tighe, Patrick (June 2021, Frontiers in Digital Health)

   Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US. 

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