Transparency Of Data Processing Research Articles

Implementation of Digital Health Ethics: A First Step with the Adoption of 16 European Ethical Principles for Digital Health.

The potential for technology to transform health care is tremendous, but advances in digital health may also bring privacy and data security challenges that may exacerbate inequalities. Hence, it is critical that the development of digital health is included in a framework of humanistic and ethical values. France drew up its roadmap for accelerating the shift towards digital health with ethics at the forefront, along with security and interoperability pillars. Criteria such as digital health for all, transparency of data processing, trustworthy AI, and eco-responsibility and sustainability of digital health were elaborated. Under the French Presidency of the Council of the European Union, building on the proposal of ethical criteria from France, eHealth network representatives unanimously adopted 16 European ethical principles for digital health, formalizing trust commitments towards European citizens and paving the way for the European Health Data Space.

Integrated diffusion image operator (iDIO): A pipeline for automated configuration and processing of diffusion MRI data.

The preprocessing of diffusion magnetic resonance imaging (dMRI) data involve numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for nonexpert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated diffusion image operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or nonspecialist users to adopt the state-of-art dMRI processing steps.

Child-friendly transparency of data processing in the EU: from legal requirements to platform policies

ABSTRACTVast amounts of personal data of children are collected and processed in today’s increasingly digital, connected society by public and private actors. Children do have a right to the protection of their personal data and, according to the General Data Protection Regulation (GDPR), even merit specific protection. Children should be clearly informed of and understand what happens with their data when it is collected, processed, stored and transferred. To that end, specific transparency standards require the provision of information in a clear and plain language that the child can easily understand. In this article, after having mapped these existing requirements, the privacy policies of Instagram, Snapchat and TikTok — services which are very popular with children — are evaluated. The findings suggest that such policies are still complex, long and primarily text based. In order to improve this, possible practical ways of enhancing transparency for children such as legal visualization, co-design, co-creation techniques and participatory design methods which focus on presenting legal information in a transparent and clear manner are explored.

METHODS OF IMPLEMENTATION OF BLOCKCHAIN TECHNOLOGIES ON THE BASIS OF CRYPTOGRAPHIC PROTECTION FOR THE DATA PROCESSING SYSTEM WITH CONSTRAINT AND LAGGING INTO ELECTRONIC DOCUMENT MANAGEMEN

The problem of application design by restriction and delay in ED (Electronic Document) management based on blockchain technologies to ensure a new level of security, reliability, transparency of data processing is considered. Increasing the reliability of information in systems by limiting and delaying ED management of enterprises and organizations during collecting, transmitting, storing and processing ED based on new, little-studied optimization technologies for processing blockchain-type data is a relevant and promising research topic. Important advantages of the potential use of transaction blocks built according to certain rules in systems by limiting and delaying ED are ensuring security by encrypting transactions for subsequent confirmation, the inability to make unauthorized changes due to the dependence of the current blockchain state on previous transactions, transparency and reliability of procedures due to public and distributed storage, as well as the interaction of a large number of users between without the use of “trusted intermediaries”. Studies show that when using existing algorithms for adding blocks in any system, it is possible to achieve the requirements of decentralization, openness of the entered data, the inability to change the data once entered into the system. However, mathematics-cryptographic information protection must be developed for each designed system separately. The task of providing and formulating the rules of data reliability control by limiting and delaying in ED circulation based on cryptographic methods of encrypting transaction blocks constituting the blockchain has been formulated. The approaches have been adopted as a methodology of support for systems by limiting and delaying electronic documents based on a new database architecture.

Abstract 2463: Prototype open-access biomarker knowledgebase for genetic tests for breast cancer

Abstract Cancer biomarkers have become integral components of many clinical protocols and research studies. Nonetheless there still is an urgent need for more accurate, less invasive, and cost-effective biomarker tests to advance precision oncology. Despite considerable success in the preclinical setting, most candidate biomarker tests have yet to receive regulatory clearance or approval and gain market acceptance. To better understand the challenges facing modern biomarker research and facilitate access to biomarker-related information, this study aims to delineate the paths to approval of genetic tests for breast cancer. Public databases were searched to identify approved tests and respective biomarkers. A prototype format for unified biomarker information was developed iteratively due to the highly variable presentation of this data. A knowledgebase of FDA-approved genetic biomarkers for breast cancer was built as a pilot implementation of this format. This model is extensible to a larger database of biomarkers to be hosted in OncoMX (https://oncomx.org), which integrates cancer mutation, differential expression, literature mining, and pathway information. Fields in the prototype database were populated based on findings from the FDA website, PubMed, and other open-source databases. The resulting dataset is structured with the corresponding readme following the BioCompute Object (BCO) model. This BCO-compliant approach enables provenance capture and transparency of data processing, both of which are critical to large-scale data integration efforts. The adherence to BCO standards is expected to enhance the usability of the resulting data and to streamline the subsequent integration of multiple biomarker datasets. In the rapidly evolving field of oncologic biomarker research, this open-source biomarker knowledgebase offers clinicians and researchers a streamlined access to genetic tests information including corresponding genes, guides to evidence, clinical trial data, links to archival databases, and context for the behavior of the implicated gene in other disease and normal processes. Citation Format: Dara Baker, Natalie Fedorova Abrams, Amanda Bell, Maureen Colbert, Hayley Dingerdissen, Evan Holmes, Samir Gupta, Robel Kahsay, Heather Kincaid, David Liu, A. S. M. Ashique Mahmood, Frédéric B. Bastian, Marc Robinson-Rechavi, Elena Schwartz, K. Vijay-Shanker, Daniel Crichton, Raja Mazumder. Prototype open-access biomarker knowledgebase for genetic tests for breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2463.

Research on Chinese Personal Information Protection Legislation in the Era of Big Data

In Chinese personal information protection legislation, consent should not be the sole legal basis for data processing, and data processing under regulation should be encouraged. At the same time, the accountability of data controllers and the transparency of data processing should be improved, and risk-based approaches should be introduced to provide proportional, situational and low-cost regulatory regulation.

Computation of adherence to medication and visualization of medication histories in R with AdhereR: Towards transparent and reproducible use of electronic healthcare data

Adherence to medications is an important indicator of the quality of medication management and impacts on health outcomes and cost-effectiveness of healthcare delivery. Electronic healthcare data (EHD) are increasingly used to estimate adherence in research and clinical practice, yet standardization and transparency of data processing are still a concern. Comprehensive and flexible open-source algorithms can facilitate the development of high-quality, consistent, and reproducible evidence in this field. Some EHD-based clinical decision support systems (CDSS) include visualization of medication histories, but this is rarely integrated in adherence analyses and not easily accessible for data exploration or implementation in new clinical settings. We introduce AdhereR, a package for the widely used open-source statistical environment R, designed to support researchers in computing EHD-based adherence estimates and in visualizing individual medication histories and adherence patterns. AdhereR implements a set of functions that are consistent with current adherence guidelines, definitions and operationalizations. We illustrate the use of AdhereR with an example dataset of 2-year records of 100 patients and describe the various analysis choices possible and how they can be adapted to different health conditions and types of medications. The package is freely available for use and its implementation facilitates the integration of medication history visualizations in open-source CDSS platforms.

INFORMATION TRANSPARENCY IN ADMISSION SELECTION OF STATE ISLAMIC HIGHER EDUCATION IN INDONESIA USING INFORMATION TECHNOLOGY APPROACH

The right to information is one of fundamental human right essential. The transparency, availability and accessibility of information enable people to hold policy-makers to account and participate in the decision making process, promoting better governance, and reducing inefficiency and corruption. In Indonesia, there is an integrated system of student admission to State Islamic Higher Education, named SPMB-PTAIN. This kind of integrated admission selection model to enter State Islamic Higher Education in Indonesia through written examination conducted simultaneously nationwide involving 53 Islamic Higher Education Institution and coordinated by the Ministry of Religious Affairs of Republic of Indonesia. In 2013, this model are used by for about 200 thousands applicants. However, there is no such model that provides information about exams result tracking for users. These condition lead to some critical issues related to the transparency in SPMB-PTAIN exams results. The subject of this paper is proposing the monitoring system as part of system improvements of SPMB-PTAIN (named SMUPDA) in terms of monitoring and transparency. SMUPDA has been tested in SPMB-PTAIN 2013 and was made such a clear form of continuous improvement in data processing of SPMB-PTAIN. SMUPDA can also provide transparency of data processing on the implementation of SPMB-PTAIN. Therefore the similar model may be applied to other systems related to public services to provide transparency and gain the public trust of public services institutions itself.

Best data handling practices in water-related research

Data play an important role in water-related research. Based on experiences in data collection and data processing in water-related research this paper proposes – both from a computer scientist's and an environmental engineer's point of view – a set of rules for data handling: Rule 1: Protect raw data; Rule 2: Save metadata; Rule 3: Use databases; Rule 4: Separate data from processing; Rule 5: Use programming; Rule 6: Avoid redundancy; Rule 7: Be transparent; Rule 8: Use standards and naming conventions. Applying these rules (i) increases the quality of data and results, (ii) allows to prepare data for long-term usage and make data accessible to different people, (iii) makes data processing transparent and results reproducible, and (iv) saves – at least in the long run – time and effort. With this contribution the authors would like to start a discussion about best data handling practices and present a first checklist of data handling and data processing for practitioners and researchers working in the water sector.