Blockchain Papers

V. S. Anoop, S. Asharaf

No abstract is available for this record.

Halimjon Khujamatov, Nurshod Akhmedov, Lazarev Amir, Khaleel Ahmad

No abstract is available for this record.

Samuel D. Hannah, A. J. Deepa, Varghese S. Chooralil, S Sangeetha · 11 authors

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.

Valli S. Kumar, John J. Lee, Qin Hu

Abstract With an increasing affinity towards patient‐centric care, sharing real‐time sensitive data for collaboration between multiple parties with finer access control becomes critical. Most existing studies based on the blockchain technology in the medical field discuss various application scenarios and security aspects, without focusing on data ownership, secure data sharing, or finer access control. In this work, a non‐fungible token (NFT)‐based system is proposed to implement a health record marketplace. The system leverages the NFT technology to provide dual ownership along with finer access control and efficiency in data sharing. The advantage of permissioned blockchain along with InterPlanetary File System (IPFS) are taken for off‐chain data storage to improve security and efficiency. Because price determination is critical in the market, Stackelberg game theory is utilized to determine pricing strategies for both data owners and consumers. Also, to efficiently achieve finer access control, a popularity‐based adaptive NFT management scheme using reinforcement learning is proposed. Simulation experiments are carried out to demonstrate accuracy and efficiency of our proposed schemes.

I Gusti Ayu Kusdiah Gemeliarana, Delphi Hanggoro, Riri Fitri Sari, Daniela M. Romano

Medical records are files that contain records and documents about patients’ identities, examinations, treatments and actions, and other services while the patients are receiving health services. Medical records are personal data that can only be accessed by authorized personnel. Some problems occur, such as the difficulty in managing files for grouping patients’ medical record data based on specific categories such as the documentation year or patients’ biodata. A large storage area is needed to hold medical record data and the absence of backup data in data storage. Blockchain-based solutions help overcome this problem. Hyperledger Composer is an open and extensive development framework for blockchain systems. Hyperledger Composer allows us to quickly model business networks and integrate them into an application or system that we have created easy and save time. Hyperledger consists of 3 main files, model file, script file, and access control file, which have coding flexibility and are easier to understand. Hyperledger Composer uses the JavaScript language for coding and has a client library for node.js. In this study, we propose a medical record sharing model that combines blockchain with the Hyperledger Composer and conducts a performance evaluation of the implementation. The result shows the increase of transactions during testing in which the system could store data into blocks to record medical transactions.

Guoming Zhang, Xuyun Zhang, Muhammad Bilal, Wanchun Dou · 6 authors

No abstract is available for this record.

Chao Chen, Yifan Shen, Guixiang Ma, Xiangnan Kong · 7 authors

Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradient-based explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We propose an optimization framework that derives global invariance from unlabeled data using self-learning to promote the stability of local explanations tailored for specific query-reference pairs. The optimization problems can be solved using gradient descent-ascent (GDA) for constrained optimization, or SGD for KL-divergence regularized unconstrained optimization, with convergence proofs, especially when the objective functions are nonconvex due to the Siamese architecture. Quantitative results and case studies on tabular and graph data from neuroscience and chemical engineering show that the framework respects the self-learned invariance while robustly optimizing the faithfulness and simplicity of the explanation. We further demonstrate the convergence of GDA experimentally.

M. Supriya, Vijay Kumar Chattu

Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.

Sarada Kiranmayee Tadepalli, Ruppa K. Thulasiram

No abstract is available for this record.

Alaa Haddad, Mohamed Hadi Habaebi, Md. Rafiqul Islam, Suriza Ahmad Zabidi

Nowadays, with large quantities of data in every industry and the advancement of technology, solutions to a wide range of problems can be resolved. In this paper, Machine Learning and Blockchain are used to propose a solution to difficulties linked to healthcare data management systems. Machine Learning allows the extraction of needed-only information that is relevant from data. This is achieved using trained algorithms that provide an intelligent decision strategy based on Convolutional Neural Networks (CNN) to automatically extract high-level semantic information from electronic medical records and then perform automatic diagnosis. Once medical data is saved, the next issue is data sharing and reliability. This is where Blockchain technology comes into play. The Blockchain with consensus protocol ensures that information is authentic, and transactions are safe. By putting the patient at the center of the healthcare system and boosting the privacy and interoperability of health data, this proposed solution can improve health care administration. This paper focuses on using Blockchain technology to solve healthcare data management problems while also incorporating some essential Machine Learning features. The expected result of the proposed system 98.67% accuracy and 96.02% recall, demonstrating that employing a convolutional neural network to learn high-level semantic aspects of electronic medical records and then undertake assist diagnosis is feasible and valuable.

Carlo Alessandro Sirianni, Parisa Sabbagh, Francesco Marra

No abstract is available for this record.

Huang Avery W, Adharsh Kandula, Xiaodi Wang

As humanity enters the information age, the amount of digitized personal information grows daily. With growing connections between the digital and real-world, privacy information becomes more and more at risk, most especially information pertaining to one's electronic health records, or EHRs. The consequences of improper EHR security are shown in [12] and [13]. Innovations in security, namely Blockchain and differential privacy, provide data centers a powerful tool to combat would-be belligerents and secure patient data. We propose a novel blockchain architecture that utilizes the discrete M-band wavelet transform with Laplace-Sigmoid noise that allows connected centers to perform relevant research while also securing sensitive EHRs and protecting patient identities. We then simulate training machine learning models using our system and show that they perform with high accuracy.

S. Vijayalakshmi, SAVITA SAVITA, S. P. Gayathri, S. Janarthanan

No abstract is available for this record.

Mario Ciampi, Angelo Esposito, Fabrizio Marangio, Mario Sicuranza · 5 authors

No abstract is available for this record.

Ammar Ibrahim El Sayed, Mahmoud Abdelaziz, Mohamed Helmy Megahed, Mohamed Hassan Abdel Azeem

According to Medical Statistics conducted in the USA, the number of patients who die every year from Medical errors are estimated to be around 200 to 400 thousand. The most common types of medical mistakes are (Billing errors, incorrect medication/incorrect dosage). Moving towards Electronic Health Records (EHR) instead of the paper-based system can prevent Medical errors. The (EHR) faces challenges regarding the issues of security and privacy. Utilizing blockchain can provide the proper solution to the above-stated challenges. This paper aims to introduce a new supervising strategy regarding accessing, sharing, and storing the (EHR). The proposed strategy enhances the security in a distributed network environment, through applying blockchain technology and hash table. Furthermore, it is applying a newly developed collision-resistant hash function in generating a unique ID for each patient in the EHR System. The Proposed scenario will improve the overall performance of the EHR system making it more efficient and reliable than the traditional system.

Alexander Pilozzi, Xudong Huang

Alzheimer's disease (AD) imposes a considerable burden on those diagnosed. Faced with a neurodegenerative decline for which there is no effective cure or prevention method, sufferers of the disease are subject to judgement, both self-imposed and otherwise, that can have a great deal of effect on their lives. The burden of this stigma is more than just psychological, as reluctance to face an AD diagnosis can lead people to avoid early diagnosis, treatment, and research opportunities that may be beneficial to them, and that may help progress towards fighting AD and its progression. In this review, we discuss how recent advents in information technology may be employed to help fight this stigma. Using artificial intelligence (AI) technologies, specifically natural language processing (NLP), to classify the sentiment and tone of texts, such as those of online posts on various social media sites, has proven to be an effective tool for assessing the opinions of the general public on certain topics. These tools can be used to analyze the public stigma surrounding AD. Additionally, there is much concern among individuals that an AD diagnosis, or evidence of pre-clinical AD such as a biomarker or imaging test results, may wind up unintentionally disclosed to an entity that may discriminate against them. The lackluster security record of many medical institutions justifies this fear to an extent. Adopting more secure and decentralized methods of data transfer and storage, and giving patients enhanced ability to control their own data, such as a blockchain-based method, may help to alleviate some of these fears.

Vivian Brian Lobo, Jetso Analin, Ronald Melwin Laban, Shraddha S. More

Owing to enlarged digital data obtainability and artificial intelligence (AI) progressions, there are quite a few occasions that can be reconnoitered in healthcare. Deep learning (DL) and inductive transfer practices are turning healthcare data- such as phantasmagorias and videotapes-into powerful data sources for predictive analytics. At the present time, patients fail to have entree to his/her individual medicinal records and hang around ignorant of data importance or prominence. This paper directs to offer a gestalt of AI and blockchain and exhibit a roadmap for a blockchain-assisted decentralized bionetwork of private healthcare data to expediate new methodologies to drug discovery and precautionary healthcare. A protected and crystal-clear disseminated marketplace of personal data by means of blockchain and DL technology will circumvent challenges faced by authorities of a given healthcare system and restore custody all across private records that includes medicinal documents back to humans. It also proposes a novel type of utility cryptotoken named LifeCoin, which can be produced through the stationing of data on the blockchain-assisted open market to streamline transactions and expedite inventive reward schemes.

Tsung-Ting Kuo, Rodney A. Gabriel, Krishna R. Cidambi, Lucila Ohno‐Machado

OBJECTIVE: Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional data protection policies, many distributed methods keep the data locally but rely on a central server for coordination, which introduces risks such as a single point of failure. We focus on providing an alternative based on a decentralized approach. We introduce the idea using blockchain technology for this purpose, with a brief description of its own potential advantages/disadvantages. MATERIALS AND METHODS: We explain how our proposed EXpectation Propagation LOgistic REgRession on Permissioned blockCHAIN (ExplorerChain) can achieve the same results when compared to a distributed model that uses a central server on 3 healthcare/genomic datasets, and what trade-offs need to be considered when using centralized/decentralized methods. We explain how the use of blockchain technology can help decrease some of the problems encountered in decentralized methods. RESULTS: We showed that the discrimination power of ExplorerChain can be statistically similar to its counterpart central server-based algorithm. While ExplorerChain inherited some benefits of blockchain, it had a small increased running time. DISCUSSION: ExplorerChain has the same prerequisites as a distributed model with a centralized server for coordination. In a manner similar to secure multi-party computation strategies, it assumes that participating institutions are honest, but "curious." CONCLUSION: When evaluated on relatively small datasets, results suggest that ExplorerChain, which combines artificial intelligence and blockchain technologies, performs as well as a central server-based method, and may avoid some risks at the cost of efficiency.

Hsin Hua Kung, Yafang Cheng, Hsiu-An Lee, Chien‐Yeh Hsu

No abstract is available for this record.

Kim Hun Ki, Jonghyun Kim, Dong Hoon Lee

No abstract is available for this record.

Pronaya Bhattacharya, Sudeep Tanwar, Umesh Bodkhe, Sudhanshu Tyagi · 5 authors

Electronic Health Records (EHRs) allows patients to control, share, and manage their health records among family members, friends, and healthcare service providers using an open channel, i.e., Internet. Thus, privacy, confidentiality, and data consistency are major challenges in such an environment. Although, cloud-based EHRs addresses the aforementioned discussions, but these are prone to various malicious attacks, trust management, and non-repudiation among servers. Hence, blockchain-based EHR systems are most popular to create the trust, security, and privacy among healthcare users. Motivated from the aforementioned discussions, we proposes a framework called as Blockchain-Based Deep Learning as-a-Service (BinDaaS). It integrates blockchain and deep-learning techniques for sharing the EHR records among multiple healthcare users and operates in two phases. In the first phase, an authentication and signature scheme is proposed based on lattices-based cryptography to resist collusion attacks among N-1 healthcare authorities from N. In the second phase, Deep Learning as-a-Service (DaaS) is used on stored EHR datasets to predict future diseases based on current indicators and features of patient. The obtained results are compared using various parameters such as accuracy, end-to-end latency, mining time, and computation and communication costs in comparison to the existing state-of-the-art proposals. From the results obtained, it is inferred that BinDaaS outperforms the other existing proposals with respect to the aforementioned parameters.

Chayakrit Krittanawong, Albert J. Rogers, Mehmet Aydar, Edward Choi · 7 authors

No abstract is available for this record.

Ray Hylock, Xiaoming Zeng

BACKGROUND: Blockchain has the potential to disrupt the current modes of patient data access, accumulation, contribution, exchange, and control. Using interoperability standards, smart contracts, and cryptographic identities, patients can securely exchange data with providers and regulate access. The resulting comprehensive, longitudinal medical records can significantly improve the cost and quality of patient care for individuals and populations alike. OBJECTIVE: This work presents HealthChain, a novel patient-centered blockchain framework. The intent is to bolster patient engagement, data curation, and regulated dissemination of accumulated information in a secure, interoperable environment. A mixed-block blockchain is proposed to support immutable logging and redactable patient blocks. Patient data are generated and exchanged through Health Level-7 Fast Healthcare Interoperability Resources, allowing seamless transfer with compliant systems. In addition, patients receive cryptographic identities in the form of public and private key pairs. Public keys are stored in the blockchain and are suitable for securing and verifying transactions. Furthermore, the envisaged system uses proxy re-encryption (PRE) to share information through revocable, smart contracts, ensuring the preservation of privacy and confidentiality. Finally, several PRE improvements are offered to enhance performance and security. METHODS: The framework was formulated to address key barriers to blockchain adoption in health care, namely, information security, interoperability, data integrity, identity validation, and scalability. It supports 16 configurations through the manipulation of 4 modes. An open-source, proof-of-concept tool was developed to evaluate the performance of the novel patient block components and system configurations. To demonstrate the utility of the proposed framework and evaluate resource consumption, extensive testing was performed on each of the 16 configurations over a variety of scenarios involving a variable number of existing and imported records. RESULTS: The results indicate several clear high-performing, low-bandwidth configurations, although they are not the strongest cryptographically. Of the strongest models, one's anticipated cumulative record size is shown to influence the selection. Although the most efficient algorithm is ultimately user specific, Advanced Encryption Standard-encrypted data with static keys, incremental server storage, and no additional server-side encryption are the fastest and least bandwidth intensive, whereas proxy re-encrypted data with dynamic keys, incremental server storage, and additional server-side encryption are the best performing of the strongest configurations. CONCLUSIONS: Blockchain is a potent and viable technology for patient-centered access to and exchange of health information. By integrating a structured, interoperable design with patient-accumulated and generated data shared through smart contracts into a universally accessible blockchain, HealthChain presents patients and providers with access to consistent and comprehensive medical records. Challenges addressed include data security, interoperability, block storage, and patient-administered data access, with several configurations emerging for further consideration regarding speed and security.

Alex Roehrs, Cristiano André da Costa, Rodrigo da Rosa Righi, Valter Ferreira da Silva · 6 authors

No abstract is available for this record.