Use Of Medical Data Research Articles

How to Elucidate Consent-Free Research Use of Medical Data: A Case for "Health Data Literacy".

The extensive utilization of personal health data is one of the key success factors of modern medical research. Obtaining consent to the use of such data during clinical care, however, bears the risk of low and unequal approval rates and risk of consequent methodological problems in the scientific use of the data. In view of these shortcomings, and of the proven willingness of people to contribute to medical research by sharing personal health data, the paradigm of informed consent needs to be reconsidered. The European General Data Protection Regulation gives the European member states considerable leeway with regard to permitting the research use of health data without consent. Following this approach would however require alternative offers of information that compensate for the lack of direct communication with experts during medical care. We therefore introduce the concept of "health data literacy," defined as the capacity to find, understand, and evaluate information about the risks and benefits of the research use of personal health data and to act accordingly. Specifically, health data literacy includes basic knowledge about the goals and methods of data-rich medical research and about the possibilities and limits of data protection. Although the responsibility for developing the necessary resources lies primarily with those directly involved in data-rich medical research, improving health data literacy should ultimately be of concern to everyone interested in the success of this type of research.

Federated Fuzzy Clustering for Decentralized Incomplete Longitudinal Behavioral Data.

The use of medical data for machine learning, including unsupervised methods such as clustering, is often restricted by privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA). Medical data is sensitive and highly regulated and anonymization is often insufficient to protect a patient's identity. Traditional clustering algorithms are also unsuitable for longitudinal behavioral health trials, which often have missing data and observe individual behaviors over varying time periods. In this work, we develop a new decentralized federated multiple imputation-based fuzzy clustering algorithm for complex longitudinal behavioral trial data collected from multisite randomized controlled trials over different time periods. Federated learning (FL) preserves privacy by aggregating model parameters instead of data. Unlike previous FL methods, this proposed algorithm requires only two rounds of communication and handles clients with varying numbers of time points for incomplete longitudinal data. The model is evaluated on both empirical longitudinal dietary health data and simulated clusters with different numbers of clients, effect sizes, correlations, and sample sizes. The proposed algorithm converges rapidly and achieves desirable performance on multiple clustering metrics. This new method allows for targeted treatments for various patient groups while preserving their data privacy and enables the potential for broader applications in the Internet of Medical Things.

Integration of Ethereum Blockchain with Cloud Computing for Secure Healthcare Data Management System

Effective management of health data is critical in this age of digital change. In order to solve the issues with safe healthcare data systems, this paper suggests a brand-new Hybrid Healthcare Data Management System (HDMS) that seamlessly combines blockchain and cloud computing technology. The solution guarantees the safe storage and effective administration of health data by utilizing the scalability and flexibility of cloud computing. The Ethereum blockchain, which offers immutability through smart contracts for data integrity assurance, is used to improve security. Important components of the suggested approach include Blockchain Anchoring, which uses distinct hash references on Ethereum to ensure data integrity, Google Cloud Integration for scalable storage and immediate data access, and compliance with Health Level 7 (HL7) formatting criteria for medical data storage. Robust privacy safeguards include cutting-edge methods like decentralized identifiers (DIDs) and homomorphic encryption, which guarantee secure computations on encrypted data and trustworthy identification for allowed access. IPFS file storage is used by the system to improve security by providing redundancy and resilience. By utilizing blockchain's immutability and cloud storage's distributed architecture, HDMS demonstrates resilience against disturbances. Results of the proposed method is implemented in Python Software. The Homomorphic approach that has been suggested continuously beats Optimized Blowfish Algorithm (OBA) in terms of encryption and decryption times. The improvement in encryption is between 3000ms (10 kb) and 1350ms (40 kb). The enhancement demonstrates improved efficiency across data sizes, ranging from 4000ms (10 kb) to 3350ms (40 kb) in decryption. The goal of HDMS is to create a future in which patient outcomes are enhanced by the appropriate handling, storage, analysis, and use of medical data.

Collaborative Semantic Annotation Tooling (CoAT) to Improve Efficiency and Plug-and-Play Semantic Interoperability in the Secondary Use of Medical Data: Concept, Implementation, and First Cross-Institutional Experiences

The cross-institutional secondary use of medical data benefits from structured semantic annotation, which ideally enables the matching and merging of semantically related data items from different sources and sites. While numerous medical terminologies and ontologies, as well as some tooling, exist to support such annotation, cross-institutional data usage based on independently annotated datasets is challenging for multiple reasons: the annotation process is resource intensive and requires a combination of medical and technical expertise since it often requires judgment calls to resolve ambiguities resulting from the non-uniqueness of potential mappings to various levels of ontological hierarchies and relational and representational systems. The divergent resolution of such ambiguities can inhibit joint cross-institutional data usage based on semantic annotation since data items with related content from different sites will not be identifiable based on their respective annotations if different choices were made without further steps such as ontological inference, which is still an active area of research. We hypothesize that a collaborative approach to the semantic annotation of medical data can contribute to more resource-efficient and high-quality annotation by utilizing prior annotational choices of others to inform the annotation process, thus both speeding up the annotation itself and fostering a consensus approach to resolving annotational ambiguities by enabling annotators to discover and follow pre-existing annotational choices. Therefore, we performed a requirements analysis for such a collaborative approach, defined an annotation workflow based on the requirement analysis results, and implemented this workflow in a prototypical Collaborative Annotation Tool (CoAT). We then evaluated its usability and present first inter-institutional experiences with this novel approach to promote practically relevant interoperability driven by use of standardized ontologies. In both single-site usability evaluation and the first inter-institutional application, the CoAT showed potential to improve both annotation efficiency and quality by seamlessly integrating collaboratively generated annotation information into the annotation workflow, warranting further development and evaluation of the proposed innovative approach.

Challenges in Collecting and Reporting Health Statistics in Kosovo.

Statistical data play a crucial role in research, planning and decision making in the health system, especially in the formulation and implementation of health policies. Health policy, adequate health legislation and sustainable financing of the health system remain serious challenges for countries in transition and especially for the Republic of Kosovo. To analyze the challenges of collection, accuracy and accurate reporting and the impact of the accuracy of these health statistics on the creation of appropriate public health policies in Kosovo. This research is mainly based on a combination of qualitative, quantitative, deductive, analytical and comparative methods conducted by health workers at all three levels of the health organization and IT experts who were actively involved in some of the important components of our research. It is interesting to note that respondents gave typical scores for the security and confidentiality of medical data and statistics during the cycle of their collection and processing, with one score being 3 and the other 5, and 50% of respondents gave a score of 5 for the successful fulfilment of the mission and tasks established by the Law on the rational use of medical data and statistics to support public health policy. The research and findings provide sufficient evidence that Kosovar health data and statistics system suffers from serious deficiencies ranging from the legal framework, organizational structure, functional organization, segregation of duties and responsibilities, to a pronounced lack of human resources, professional profiles in the health statistics service and the necessary technology to perform the basic tasks in a timely and high quality manner.

Blockchain-Based Data Access Security Solutions for Medical Wearables.

Digital healthcare services have become an integral part of our lives. There is an increasing number of healthcare professionals and patients using medical wearables for diagnosis and treatment, which simplifies and improves the diagnostic and therapeutic process. However, inappropriate use of medical data may result in the disclosure of private patient information. For protecting patients' privacy when using medical wearables, we propose a new blockchain-based data access security scheme. Specifically, the elliptic curve encryption algorithm and zero-knowledge authentication method are used to authenticate the identity of patients and doctors in the blockchain network. Furthermore, we develop a smart recommendation method based on deep reinforcement learning to recommend appropriate doctors for patients. Next, patients allow recommended doctors to access their medical data, and smart contracts specifically designed for secure data access to medical wearables will regulate subsequent data access. The security analysis and experimental results demonstrate that the proposed scheme can effectively protect patients' privacy during treatment through secure authentication and data access for medical wearables.

Endomapper dataset of complete calibrated endoscopy procedures

Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures is needed. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, making secondary use of medical data. Its main purpose is to facilitate the development and evaluation of Visual Simultaneous Localization and Mapping (VSLAM) methods in real endoscopy data. The dataset contains more than 24 hours of video. It is the first endoscopic dataset that includes endoscope calibration as well as the original calibration videos. Meta-data and annotations associated with the dataset vary from the anatomical landmarks, procedure labeling, segmentations, reconstructions, simulated sequences with ground truth and same patient procedures. The software used in this paper is publicly available.

Patient data for commercial companies? An ethical framework for sharing patients’ data with for-profit companies for research

BackgroundResearch using data from medical care promises to advance medical science and improve healthcare. Academia is not the only sector that expects such research to be of great benefit. The...

ПРАКТИЧЕСКАЯ ЭТИКА СФЕРЫ МЕДИЦИНСКИХ ДАННЫХ В ЦИФРОВУЮ ЭПОХУ: ПСИХОЛОГИЧЕСКИЙ АСПЕКТ

The aim of the study is to study the ethical implications of the processing and use of medical data in the digital age, with particular attention to the psychological aspect, as well as understanding the impact of medical data management practices on the psychological well-being of individuals and communities. Medical digital data is an integral part of healthcare and is increasingly becoming a daily routine. The digital age has brought with it many new opportunities, such as accessing medical data in real time, storing it in vast databases and using it to inform decisions. However, these advances raise ethical questions about how data is managed and used by whom. The article explores the need for practical ethics in the processing of medical data, and sets out key principles and considerations. The study aims to provide a comprehensive understanding of the ethical and psychological implications of medical data management practices in the digital age, including the potential advantages and disadvantages of these practices. By examining these issues from a psychological perspective, the study aims to provide valuable insights on how to balance the benefits of using medical data with the need to protect people's rights.

ECardiology: a structured approach to foster the digital transformation of cardiovascular medicine

The use of digital technologies in medicine is developing largely in parallel with the IT industry. Today, every cardiologist in Germany uses IT systems to make appointments, store patient data and write doctor’s letters. So why is it necessary to introduce a dedicated eCardiology program within the German Society of Cardiology (DGK)? So far, digitalization has failed in many respects to fulfill its promise of making the daily lives of physicians and patients easier. Clinical EDP solutions derive mainly from accounting systems and lead only to a limited extent to an improvement in patient care. Negative effects on burnout rates and job satisfaction of medical staff could even be observed. Additionally, since May 2018 the introduction of the European General Data Protection Regulation with its principles of purpose limitation and data minimization has made the storage and use of medical data more difficult. This also has fundamental implications for developments in artificial intelligence (AI), which are based on the availability and quality of big data (= large, complex data sets) and could permanently change the healthcare system. The technology-driven progress of medicine does not end at the doorstep of a hospital but causes a significant cultural change in our society. The use of so-called wearables, search engines on the Internet and social media is increasingly enabling e‑patients to make their own diagnoses and independent decisions regarding their health. As a result, physicians often see well-informed, but also misinformed patients who have been advised on health-related data by their own smart devices. The DGK recognizes the enormous challenges and potential of digital medicine to improve the prognosis of cardiovascular diseases. As part of the eCardiology program, the Society has established five committees to promote and communicate important aspects of digital health: transsectoral collaboration, mobile health, precision digital health, society and policy, and education and media. We report here on the elements of each committee and its working groups.

Digitalisierung im Gesundheitswesen: heute und in Zukunft

Although Germany continues to struggle with the digital transformation of healthcare, there is reason for optimism. The political will to improve healthcare with digital technologies has been underpinned by numerous legal initiatives since 2018. In addition, there is growing acceptance among healthcare providers and the population. The latter has clearly been driven by the corona pandemic, which underscored the need for more digitized care.Digitalization in healthcare has three key drivers: the rapid technological development in data processing, the ever-improving understanding of the biological basis of human life, and growing patient sovereignty coupled with agrowing desire for transparency. Prerequisites for digital medicine are data interoperability and the establishment of anetworking (telematics) infrastructure (TI). The status of the most important digital TI applications affecting German healthcare are described: the electronic patient record (ePA) as its core as well as electronic prescriptions, medication plans, and communication tools such as Communication in Medicine (KIM) and TI Messenger (TIM). In addition, various telemedical offerings are discussed as well as the introduction of digital health applications (DiGA) into the statutory healthcare system, which Germany has pioneered. Furthermore, the use of medical data as the basis for artificial intelligence (AI) algorithms is discussed. While helpful and capable of improving diagnostics as well as medical therapy, such AI tools will not replace doctors and nurses.

ECardiology: ein strukturierter Ansatz zur Förderung der digitalen Transformation in der Kardiologie

The use of digital technologies in medicine is developing largely in parallel with the IT industry. Today, every cardiologist in Germany uses IT systems to make appointments, store patient data and write doctor’s letters. So why is it necessary to introduce a dedicated eCardiology program within the German Society of Cardiology (DGK)? So far, digitalization has failed in many respects to fulfill its promise of making the daily lives of physicians and patients easier. Clinical EDP solutions derive mainly from accounting systems and lead only to a limited extent to an improvement in patient care. Negative effects on burnout rates and job satisfaction of medical staff could even be observed. Additionally, since May 2018 the introduction of the European General Data Protection Regulation with its principles of purpose limitation and data minimization has made the storage and use of medical data more difficult. This also has fundamental implications for developments in artificial intelligence (AI), which are based on the availability and quality of big data (= large, complex data sets) and could permanently change the healthcare system. The technology-driven progress of medicine does not end at the doorstep of a hospital but causes a significant cultural change in our society. The use of so-called wearables, search engines on the Internet and social media is increasingly enabling e‑patients to make their own diagnoses and independent decisions regarding their health. As a result, physicians often see well-informed, but also misinformed patients who have been advised on health-related data by their own smart devices. The DGK recognizes the enormous challenges and potential of digital medicine to improve the prognosis of cardiovascular diseases. As part of the eCardiology program, the Society has established five committees to promote and communicate important aspects of digital health: transsectoral collaboration, mobile health, precision digital health, society and policy, and education and media. We report here on the elements of each committee and its working groups.

Special Issue on Novel Applications of Artificial Intelligence in Medicine and Health

Artificial Intelligence (AI) is one of the big hopes for the future of a positive revolution in the use of medical data to improve clinical routine and personalized medicine [...]

Design of Intelligent Question Answering System for Hospital Online Triage based on Knowledge Graph

At present, people pay more and more attention to medical and health issues, and more and more Internet users are eager to obtain medical related knowledge from the Internet. However, despite the large amount of medical data spread on the Internet, it is difficult for ordinary Internet users to effectively obtain and use these data through search engines. To solve this problem, this paper designs and implements a medical question answering system based on knowledge map, so that users can quickly obtain the relevant knowledge in the medical field they want by asking questions, and realize the effective use of medical data on the Internet. At present, there are few question answering systems in the medical and health field, so the medical question answering system designed in this paper has a broad application prospect. The medical knowledge map constructed in the system can also be used in more application scenarios such as medical chat robots in the future, further promoting the process of intelligence in the medical field.

Blockchain Management and Federated Learning Adaptation on Healthcare Management System

Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined. Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.

Secure medical image sharing for smart healthcare system based on cellular neural network

The smart healthcare system (SHCS) facilitates the healthcare process with the widespread use of medical data through the internet of medical things (IoMT). Widespread use of medical data, especially medical images can also lead to privacy issues. Traditional encryption algorithms can address some problems; however, they cannot deter the redistribution of decrypted content. To prevent the decrypted content from being used illegally, the paper proposes a combination scheme of encryption and fingerprinting based on the game of life (GOL) and singular value decomposition (SVD) with the purpose of protecting medical images. First, medical images are performed with discrete wavelet transform (DWT). Second, the highest coefficients bit planes of the approximation component are selected to confuse with GOL. Third, the other bit planes and other detail components are chosen to embed fingerprints. Finally, all subbands are diffused with SVD computing. The proposed privacy protection scheme, as far as we know, is the first privacy protection scheme for the SHCS using GOL and SVD based on a chaotic cellular neural network (CNN). The proposed scheme can provide double-level privacy protection for the SHCS. The experimental results and discussion verify it is effective for the privacy protection of the SHCS.

A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography.

Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D computed tomography (CT) images is a cumbersome task even for the trained experts, automatic 3D landmark detection system is in a great need. Recently, automatic landmarking of 2D cephalograms using deep learning (DL) has achieved great success, but 3D landmarking for more than 80 landmarks has not yet reached a satisfactory level, because of the factors hindering machine learning such as the high dimensionality of the input data and limited amount of training data due to the ethical restrictions on the use of medical data. This paper presents a semi-supervised DL method for 3D landmarking that takes advantage of anonymized landmark dataset with paired CT data being removed. The proposed method first detects a small number of easy-to-find reference landmarks, then uses them to provide a rough estimation of the all landmarks by utilizing the low dimensional representation learned by variational autoencoder (VAE). The anonymized landmark dataset is used for training the VAE. Finally, coarse-to-fine detection is applied to the small bounding box provided by rough estimation, using separate strategies suitable for the mandible and the cranium. For mandibular landmarks, patch-based 3D CNN is applied to the segmented image of the mandible (separated from the maxilla), in order to capture 3D morphological features of mandible associated with the landmarks. We detect 6 landmarks around the condyle all at once rather than one by one, because they are closely related to each other. For cranial landmarks, we again use the VAE-based latent representation for more accurate annotation. In our experiment, the proposed method achieved a mean detection error of 2.88 mm for 90 landmarks using only 15 paired training data.

Use of medication data alone to identify diagnoses and related contraindications: Application of algorithms to close a common documentation gap.

Automated checks for medication-related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug-drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug-disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses. We identified drugs that are associated with diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert-based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real-life sample of medication and diagnosis data excerpts from 3506 discharge records of geriatric patients. Depending on the algorithm, the desired focus (i.e., sensitivity vs. specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0-99.8% (95% CI 41.7-100.0%) and a sensitivity of 3.8-83.1% (95% CI 1.0-86.1%). Using only medication data, we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses. This study provides a proof of principle that some key diagnosis-related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug-disease contraindications in community pharmacy or clinical routine data.

1．Millennial Medical Record Project: Secondary Use of Medical Data for Research and Development Based on the Next Generation Medical Infrastructure Law

EHR (Electronic Health Record) の始まりは，1995 年から2000 年の医療情報共通規格 (MML： Medical Markup Language) の開発にさかのぼることができる．2001年に，MML をデータベース構造としたEHR が開発され，熊本，宮崎を基点に東京，京都へ拡大していった (Dolphin Project)．その後，国家レベルでの医療情報管理の必要性が認識され，また，医療情報の二次利用の必要性も認識され，2015年に国レベル版EHR である「千年カルテプロジェクト」が始まった．2018年度までの4年間で接続医療機関数が106となった．2019年12月に一般社団法人ライフデータイニシアティブが，2018 年に施行された「医療分野の研究開発に資するための匿名加工医療情報に関する法律，以下 “次世代医療基盤法”」による第1号認定企業となり，補助金に依存しない，EHR 部門も含めた独立採算を目指して運営中である．

Imputation and system modeling of acid-base state parameters for different groups of patients

The paper investigated the possibility of correct replacement of missing values in sets of acid-base state in the artery and vein in different groups of patients with different outcomes of the disease: “discharged”, “died”, “transferred to another medical institution”, as well as prospects for the application of individual optimization multidimensional estimates of these biomedical parameters in the form of projections on a one-dimensional space. The relevance of the above tasks is determined by the need for the full use of medical data in the analysis of large repositories of information of medical organizations and the provision of verified multidimensional assessments of biomedical systems to doctors from a large range of patient health indicators. A statistical method has been applied to verify the correctness of imputation data sets using discriminant analysis procedures. Further, the imputed data set was processed to obtain a symmetric correlation matrix optimized in a certain way and the accompanying logarithms of criterion functions that are individual system assessments of the condition of each patient in different groups of patients at a certain point in the study. After that, to identify differences in the logarithms of the criterion functions of the acid-base state parameters between groups of patients with different outcomes, the authors used the method of calculating multidimensional Hotelling T2 statistics. The correctness of the application of discriminant analysis procedures to verify the imputation of data sets is shown. Differences in the logarithms of the criteria functions of the acid-base state indicators between venous and arterial blood by patient outcome groups were revealed. Significant differences in the parameters of acid-base state based on the multidimensional statistics of T2 Hotelling between groups of patients with different outcomes were revealed. It is found that data imputation significantly increases the volume and representativeness of the sample under study. It is demonstrated that the substituted data make it possible to carry out a systematic statistical assessment of the totality of body parameters based on the calculation of the logarithms of the criterion functions of the acid-base state. Such logarithms make it possible to reliably distinguish patients in different groups of patients by outcomes in three groups: “discharged”, “deceased”, “transferred to another medical institution”. 100 % differences of biochemical parameters according to the multivariate T2 Hotelling statistics between these three groups of patients with COVID-19 are shown. The results of the study can be applied in the development of information systems of individual medical biochemical and hematological devices and analyzers and corresponding artificial intelligence systems in the future.