Medical Data Integration Research Articles

Feasibility Queries in Distributed Architectures - Concept and Implementation in HiGHmed.

Medical routine data promises to add value for research. However, the transfer of this data into a research context is difficult. Therefore, Medical Data Integration Centers are being set up to merge data from primary information systems in a central repository. But, data from one organization is rarely sufficient to answer a research question. The data must be merged beyond institutional boundaries. In order to use this data in a specific research project, a researcher must have the possibility to query available cohort sizes across institutions. A possible solution for this requirement is presented in this paper, using a process for fully automated and distributed feasibility queries (i.e. cohort size estimations). This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4 resources. The proposed solution is currently being deployed at eight university hospitals and one trusted third party across Germany.

Needs for an Integration of Specific Data Sources and Items - First Insights of a National Survey Within the German Center for Infection Research.

State-subsidized programs develop medical data integration centers in Germany. To get infection disease (ID) researchers involved in the process of data sharing, common interests and minimum data requirements were prioritized. In 06/2019 we have initiated the German Infectious Disease Data Exchange (iDEx) project. We have developed and performed an online survey to determine prioritization of requests for data integration and exchange in ID research. The survey was designed with three sub-surveys, including a ranking of 15 data categories and 184 specific data items and a query of available 51 data collecting systems. A total of 84 researchers from 17 fields of ID research participated in the survey (predominant research fields: gastrointestinal infections n=11, healthcare-associated and antibiotic-resistant infections n=10, hepatitis n=10). 48% (40/84) of participants had experience as medical doctor. The three top ranked data categories were microbiology and parasitology, experimental data, and medication (53%, 52%, and 47% of maximal points, respectively). The most relevant data items for these categories were bloodstream infections, availability of biomaterial, and medication (88%, 87%, and 94% of maximal points, respectively). The ranking of requests of data integration and exchange is diverse and depends on the chosen measure. However, there is need to promote discipline-related digitalization and data exchange.

The Role of Artificial Intelligence in Managing Multimorbidity and Cancer.

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

PSO-Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture.

Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 [COVID-19]) in efficient and secure transmission are important but challenging in communication channels amongst hospitals. In particular, in addressing the above challenges, two issues are faced, namely, those related to confidentiality and integrity of their health data and to network failure that may cause concerns about data availability. To the authors’ knowledge, no study provides secure updating and sharing solution for large amounts of healthcare information in communication channels amongst hospitals. Therefore, this study proposes and discusses a novel steganography-based blockchain method in the spatial domain as a solution. The novelty of the proposed method is the removal and addition of new particles in the particle swarm optimisation (PSO) algorithm. In addition, hash function can hide secret medical COVID-19 data in hospital databases whilst providing confidentiality with high embedding capacity and high image quality. Moreover, stego images with hash data and blockchain technology are used in updating and sharing medical COVID-19 data between hospitals in the network to improve the level of confidentiality and protect the integrity of medical COVID-19 data in grey-scale images, achieve data availability if any connection failure occurs in a single point of the network and eliminate the central point (third party) in the network during transmission. The proposed method is discussed in three stages. Firstly, the pre-hiding stage estimates the embedding capacity of each host image. Secondly, the secret COVID-19 data hiding stage uses PSO algorithm and hash function. Thirdly, the transmission stage transfers the stego images based on blockchain technology and updates all nodes (hospitals) in the network. As proof of concept for the case study, the authors adopted the latest COVID-19 research published in the Computer Methods and Programs in Biomedicine journal, which presents a rescue framework within hospitals for the storage and transfusion of the best convalescent plasma to the most critical patients with COVID-19 on the basis of biological requirements. The validation and evaluation of the proposed method are discussed.

The future of healthcare in Singapore. How an integrated use of A.I., Internet-of-Medical things (IoMT), Blockchain-based technologies, and Cloud-computing-based Medtech and Digital Health solutions will radically address medical data integrity concerns.

Industry 4.0 in the pharmaceutical, medical devices, and healthcare sectors could have a significant impact as they can help maximize productivity by analyzing the data of patients. The application of Industry 4.0 technologies to the pharma, medical, and healthcare sectors may also play an important role in addressing and helping overcome the COVID-19 pandemic, thanks to the latest innovations in the space of biopharma and biotech. As Ow (2021b) discussed and explored how medical industries in Singapore can utilize Artificial Intelligence, Blockchain, Digital health, Internet of Medical Things (IoMT), and Telemedicine as part of the future of the digital health evolution. This study will explore how the utilization of Blockchain can help to address medical data security concerns to accelerate the utilization of digital health and Telemedicine, which is one of the key concerns cited by both the general public and industry experts, which might impact the adoption of the industrial 4.0 technologies. This study will also examine how receptive the public is to use these technologies; the integration of technologies into the healthcare industry, and the future of digital health. This research focuses on the Singapore medical industry and on the opinions of Singaporeans and the predictions of industry experts. Using qualitative methodology, this research is exploratory in nature, given the early-stage developments in the implementation of integrated 4.0 technologies in the digital health space. In addition, this study aims to assess the future potential benefits of integrating these advanced technologies into the medical and biopharma industries for the healthcare system of Singapore and its citizens. Finally, this study will seek to contribute to the existing literature in the fields of digital health, Blockchain-based health devices, Internet of Medical Things (IoMT), and Telemedicine, and to the integrated use of these technologies to create new value innovation opportunities and business models in the rapidly expanding digital health space of Singapore.

PPM1 Development of a DATA Infrastructure That Combines Top-Tier Danish Registry DATA with Real-World DATA, to Create More Complete Personalized Medical DATA, While Allowing for Commercialization While Upholding Gdpr Legislation

Creating a patient-centric health data infrastructure that successfully combines superior Danish registry data with data from patients’ apps and wearables. Thus, generating personalized medical data, while upholding strict ethical and GDPR guidelines, creating value for patients through targeted medical solutions, improved ability for commercial leverage and accessible health data overview. The Hedax and OSCAR projects bring together the relevant Danish consortiums to collaborate on bridging the political and technical gap between the personal app & wearable data, Danish health data, and GDPR. The project partners have developed the technology to process Danish health registry data, e.g. economic, statistical, geo-demographic data, private data (with consent from the user) from smart devices, medical devices, insurance claim data, pseudonymized CPR registry data, and genome data. The data sources are connected to a Personal Data Store (PDS) and a virtual platform for a GDPR compliant, encrypted analysis. The outcome is a patient-centric health data infrastructure that is GDPR compliant while commercializing the extracted combination of high-quality Danish health registry data and real-world data. The result will be enhanced patient health data insights, integrated clinical pathways, personalized medicine for citizens, access to 3rd party data for healthcare providers, a GDPR data hotel, sandbox, and enhanced access to enriched health data for clinical trials and research-based innovation. Hedax & OSCAR’s new health data infrastructure that combines registry data with apps & wearables paves the way for a level of medical data integration yet to be seen on the world market and promises a wealth of societal and commercial benefits.

PathoSPOT genomic epidemiology reveals under-the-radar nosocomial outbreaks

BackgroundWhole-genome sequencing (WGS) is increasingly used to map the spread of bacterial and viral pathogens in nosocomial settings. A limiting factor for more widespread adoption of WGS for hospital infection prevention practices is the availability of standardized tools for genomic epidemiology.MethodsWe developed the Pathogen Sequencing Phylogenomic Outbreak Toolkit (PathoSPOT) to automate integration of genomic and medical record data for rapid detection and tracing of nosocomial outbreaks. To demonstrate its capabilities, we applied PathoSPOT to complete genome surveillance data of 197 MRSA bacteremia cases from two hospitals during a 2-year period.ResultsPathoSPOT identified 8 clonal clusters encompassing 33 patients (16.8% of cases), none of which had been recognized by standard practices. The largest cluster corresponded to a prolonged outbreak of a hospital-associated MRSA clone among 16 adults, spanning 9 wards over a period of 21 months. Analysis of precise timeline and location data with our toolkit suggested that an initial exposure event in a single ward led to infection and long-term colonization of multiple patients, followed by transmissions to other patients during recurrent hospitalizations.ConclusionsWe demonstrate that PathoSPOT genomic surveillance enables the detection of complex transmission chains that are not readily apparent from epidemiological data and that contribute significantly to morbidity and mortality, enabling more effective intervention strategies.

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries.

Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively.

Using openEHR in XDS.b Environments - Opportunities and Challenges.

The HiGHmed research project as part of Germany's Medical Informatics Initiative aims to establish Medical Data Integration Centers (MeDIC) at each participating institution to integrate data of multiple primary information systems in a single place. The MeDICs are based on an IHE XDS.b Affinity Domain in conjunction with an openEHR clinical data repository. This paper presents two ways of storing and retrieving structured and semantically annotated data from an openEHR repository whilst keeping data integrated in an existing IHE XDS.b infrastructure. Furthermore, the paper discusses multiple benefits of the presented approach as well as challenges and corresponding solutions.

Security and Privacy Requirements for Cloud Computing in Healthcare

Cloud computing promises essential improvements in healthcare delivery performance. However, its wide adoption in healthcare is yet to be seen, one main reason being patients’ concerns for security and privacy of their sensitive medical records. These concerns can be addressed through corresponding security and privacy requirements within the system engineering process. Despite a plethora of related research, security and privacy requirements for cloud systems and services have seldomly been investigated methodically so far, whereas their individual priorities to increase the system success probability have been neglected. Against this background, this study applies a systematic requirements engineering process: First, based on a systematic literature review, an extensive initial set of security and privacy requirements is elicited. Second, an online survey based on the best-worst scaling method is designed, conducted, and evaluated to determine priorities of security and privacy requirements. Our results show that confidentiality and integrity of medical data are ranked at the top of the hierarchy of prioritized requirements, followed by control of data use and modification, patients’ anonymity, and patients’ control of access rights. Availability, fine-grained access control, revocation of access rights, flexible access, clinicians’ anonymity, as well as usability, scalability, and efficiency of the system complete the ranking. The level of agreement among patients is rather small, but statistically significant at the 0.01 level. The main contribution of the present research comprises the study method and results highlighting the role of strong security and privacy and excluding any trade-offs with system usability. Enabling a richer understanding of patients’ security and privacy requirements for adopting cloud computing in healthcare, these are of particular importance to researchers and practitioners interested in supporting the process of security and privacy engineering for health-cloud solutions. It further represents a supplement that can support time-intensive negotiation meetings between the requirements engineers and patients.

Secure Similar Image Search and Copyright Protection over Encrypted Medical Image Databases

The recent advances in digital medical imaging and storage in cloud are bringing about more demands for efficient and secure image retrieval and management. Typically, medical images are very sensitive to changes where any modifications in its content may bring about an erroneous medical diagnosis. Therefore, securing medical images is a very essential process and the major task is, the medical image must maintain their sensitive contents at the time of reconstruction. The proposed methodology executes a secure image encryption and search of medical images proficiently over encrypted image database without leaking any sensitive data. It also ensures medical data integrity by introducing an efficient recovery mechanism on ROI of the image. The proposed scheme obtains recovery information about the image from the ROI of the medical data and embeds it in the RONI region using IWT transform which act as a reversible watermarking. If any alterations or tampers are caused to ROI at the third-party end, then it can be identified and recovered from the obtained recovery data. Besides, the model also executes a Copyright protection scheme to locate the authorized users, who illegally duplicate and distribute the retrieved image to unauthorized entities.

A novel proxy-oriented public auditing scheme for cloud-based medical cyber physical systems

Cloud-based medical cyber-physical system (MCPS) is attracting more and more attention, since it can break through the limitations of time, space and object, and allow doctors to make accurate telemedicine diagnosis for patients. However, since medical information is directly related to the safety of patients’ lives and property, it has become an urgent problem to ensure the integrity and availability of medical data. To solve the above problems, many integrity auditing schemes have been proposed in recent years. Unfortunately, most existing schemes have security flaws or poor performance. Very recently, Zhang et al. proposed an identity-based proxy-oriented auditing scheme for checking the integrity of medical data, and claimed that their scheme was secure. However, we find that their scheme fails to achieve its purpose since it is vulnerable to the user impersonation attack and misusing delegation attack. we propose a new proxy-oriented public auditing scheme to fix the security vulnerabilities. Security analysis and performance evaluation demonstrate that our proposed scheme greatly reduces the communication and computation costs while ensuring a novel security level. Compared with Zhang et al.’s scheme, our scheme is more suitable for applications in cloud-based MCPS.

Lung Cancer Detection using Bio-Inspired Algorithm in CT Scans and Secure Data Transmission through IoT Cloud

Primary recognition of pulmonary cancer nodules eloquently increases the odds of survival, also leads it solider problem to resolve, as it often relies on a tomography scan filmic examination. By increasing the possibility of effective treatment, earlier tumor diagnosis decreases lung cancer mortality. Radiologists usually diagnose lung cancer on medical images by a systematic analysis that consumes more time and is unreliable often, because of the substantial improvement in the transmission of data in the healthcare sector, the protection and integrity of medical data has been a huge problem for healthcare applications. This study utilizes computational intelligence techniques. For detection and data transmission, a novel Hybrid model is therefore proposed in this paper. Two steps are involved in the proposed method where diverse image processing procedures are used to detect cancer in the first step using MATLAB and data transfer to authorized persons via the IoT cloud in the second stage. The simulated steps include pre-processing, segmentation by Otsu thresholding along with swarm intelligence algorithm, extraction of features by local binary pattern and classification using the support vector machine (SVM). This work demonstrates the dominance of swarm-intelligent framework over the conventional algorithms in terms of performance metrics like sensitivity, accuracy and specificity as well as training time. The tests carried out show that the model built can achieve up to 92.96 percent sensitivity, 93.53 percent accuracy and 98.52 percent specificity.

SHealth: A Blockchain-Based Health System With Smart Contracts Capabilities

Governments rely on collected data to analyze their citizens' health requirements and needs. Such data is usually of scattered manner gathered by several agencies and entities that fall under different business models and goals. Hence, the scattered nature of this data creates a burden on governments in precisely planning adequate health bills that guarantee the wellbeing of their citizens. Furthermore, the issue of synchronizing and having a real-time access to such data exacerbates the problem even more. Therefore, a system that guarantees the integrity, accessibility, correctness and security of medical data in a highly synchronized environment is crucial for governments to accurately project their future needs. In this paper, we present Smart-Health (SHealth), a blockchain-based health management system. In its crux, SHealth is a private multi-layered blockchain integrated with a multi-tiered addressing scheme that defines the privileges and permissions of entities in the system. Blockchain-based systems assure security, reliability, availability, resistance against tampering and malicious attacks, seamless integration, and easy data management. SHealth proposes a complete autonomy to its users. Through a user-friendly graphical interface it capitalizes on smart contracts to initiate various requests and inquiries pertaining to patients such as appointments, medical tests, medications, medical procedures, or history. SHealth is simple, robust, efficient, secured, and completely automated. All the stakeholders in the system can access the health related data stored in a distributed database without compromising its authenticity. SHealth covers all the possible scenarios in health systems from which some of them are explained in this work.

The analysis of process of decentralization of the system of management of medical data of patients with application of cloud technologies

Actually, the opportunity exists to obtain admittance to various data. The article presents the essence of approach to development of decentralized prototype of system managing medical records of patient using blockchain technology. The global analysis of cloud technologies using various experience of their implementation is given. The scientific novelty of study consists in step-by-step application of genuine prototype of patient medical data management system using blockchain technology. It is scientifically justified that applying cloud technologies in medicine permits to improve safety and integrity of medical data of patient and to support linking uncoordinated databases into one whole, making interaction of patients and physicians much more effective and comfortable.

LDVAS: Lattice-Based Designated Verifier Auditing Scheme for Electronic Medical Data in Cloud-Assisted WBANs

Data integrity has become an extremely important security issue in cloud-assisted wireless body area networks (WBANs). As accurate medical diagnostic analysis is heavily based on outsourced electronic medical data in cloud storage, any corrupted data may lead to fateful consequences. Data auditing contributes to medical data integrity verification, but most of existing data auditing schemes are vulnerable to attackers equipped with practical quantum-computing devices, which are very likely to be invented in the near future. Besides, in many scenarios, a patient has no capability to execute medical data integrity verification personally, he/she has to specify a verifier to complete data auditing. To this end, we present an efficient lattice-based designated verifier auditing scheme (LDVAS), which could be well deployed in cloud-assisted WBANs. In particular, LDVAS enables a patient to designate a unique verifier to execute data auditing in post-quantum secure settings, and any others cannot fulfil such task without the approval of the patient. We formally prove the security of LDVAS based on the hardness assumptions of lattice-based problems. The performance evaluation demonstrates the high efficiency and feasibility of LDVAS on the side of the designated verifier.

Efficient light-weight private auditing scheme for cloud-based wireless body area networks

With the rapid development of cloud computing, cloud-based wireless body area networks (WBANs) provide powerful storage services, and process massive medical data efficiently. As the medical data are exploited to perform the clinical diagnosis and other special medical treatments, the integrity of the medical data stored in the cloud server is increasingly important. In this paper, based on the elliptic curve digital signature algorithm (ECDSA), we propose an efficient light-weight cloud storage private auditing scheme for medical data in WBANs. Our scheme enables a data owner to check the medical data integrity effectively personally, and does not need to retrieve the entire medical dataset, thereby dramatically reducing the communication overhead. Moreover, we further extend our private auditing scheme to private batch auditing. Thus, the data owner can perform auditing task for multiple different medical data files simultaneously. The performance comparison demonstrates that our scheme is much more light-weight, and more practical in cloud-based wireless body area networks.

Efficient light-weight private auditing scheme for cloud-based wireless body area networks

With the rapid development of cloud computing, cloud-based wireless body area networks (WBANs) provide powerful storage services, and process massive medical data efficiently. As the medical data are exploited to perform the clinical diagnosis and other special medical treatments, the integrity of the medical data stored in the cloud server is increasingly important. In this paper, based on the elliptic curve digital signature algorithm (ECDSA), we propose an efficient light-weight cloud storage private auditing scheme for medical data in WBANs. Our scheme enables a data owner to check the medical data integrity effectively personally, and does not need to retrieve the entire medical dataset, thereby dramatically reducing the communication overhead. Moreover, we further extend our private auditing scheme to private batch auditing. Thus, the data owner can perform auditing task for multiple different medical data files simultaneously. The performance comparison demonstrates that our scheme is much more light-weight, and more practical in cloud-based wireless body area networks.

Multi-Source Medical Data Integration and Mining for Healthcare Services

With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.

Deep learning-based automated detection of retinal diseases using optical coherence tomography images.

Retinal disease classification is a significant problem in computer-aided diagnosis (CAD) for medical applications. This paper is focused on a 4-class classification problem to automatically detect choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL in optical coherence tomography (OCT) images. The proposed classification algorithm adopted an ensemble of four classification model instances to identify retinal OCT images, each of which was based on an improved residual neural network (ResNet50). The experiment followed a patient-level 10-fold cross-validation process, on development retinal OCT image dataset. The proposed approach achieved 0.973 (95% confidence interval [CI], 0.971-0.975) classification accuracy, 0.963 (95% CI, 0.960-0.966) sensitivity, and 0.985 (95% CI, 0.983-0.987) specificity at the B-scan level, achieving a matching or exceeding performance to that of ophthalmologists with significant clinical experience. Other performance measures used in the study were the area under receiver operating characteristic curve (AUC) and kappa value. The observations of the study implied that multi-ResNet50 ensembling was a useful technique when the availability of medical images was limited. In addition, we performed qualitative evaluation of model predictions, and occlusion testing to understand the decision-making process of our model. The paper provided an analytical discussion on misclassification and pathology regions identified by the occlusion testing also. Finally, we explored the effect of the integration of retinal OCT images and medical history data from patients on model performance.