Big data management challenges in health research-a literature review.

Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.

[Opportunities and challenges of blockchain technology in the healthcare industry]. / Opportunités et enjeux de la technologie blockchain dans le secteur de la santé.

Often described as a tool to build trust among stakeholders with divergent interests, blockchain technology has been of interest to many sectors since it was first used in 2008. Initially designed to record financial transactions between individuals, its applications have largely evolved with technological advances and the growing interest of international companies. In the healthcare sector, blockchain is interesting for many of its features: its immutability which makes it an excellent support for authenticating sensitive data such as clinical trials consents, the possibility of publishing smart contracts that automate and facilitate many processes or the constitution of a network that agrees on the state of the information. Much acclaimed, blockchain technology is still to be tested in real-life conditions and adapted to a particularly complex regulatory and economic context in the healthcare sector.

Should biomedical research be like Airbnb?

The thesis presented here is that biomedical research is based on the trusted exchange of services. That exchange would be conducted more efficiently if the trusted software platforms to exchange those services, if they exist, were more integrated. While simpler and narrower in scope than the services governing biomedical research, comparison to existing internet-based platforms, like Airbnb, can be informative. We illustrate how the analogy to internet-based platforms works and does not work and introduce The Commons, under active development at the National Institutes of Health (NIH) and elsewhere, as an example of the move towards platforms for research.

Data science, learning, and applications to biomedical and health sciences.

The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self-tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data.

Advanced literature analysis in a Big Data world.

Comprehensive data mining of the scientific literature has become an increasing challenge. To address this challenge, Elsevier's Pathway Studio software uses the techniques of natural language processing to systematically extract specific biological information from journal articles and abstracts that is then used to create a very large, structured, and constantly expanding literature knowledgebase. Highly sophisticated visualization tools allow the user to interactively explore the vast number of connections created and stored in the Pathway Studio database. We demonstrate the value of this structured information approach by way of a biomarker use case example and describe a comprehensive collection of biomarkers and biomarker candidates, as reported in the literature. We use four major neuropsychiatric diseases to demonstrate common and unique biomarker elements, demonstrate specific enrichment patterns, and highlight strategies for identifying the most recent and novel reports for potential biomarker discovery. Finally, we introduce an innovative new taxonomy based on brain region identifications, which greatly increases the potential depth and complexity of information retrieval related to, and now accessible for, neuroscience research.

Distributed data networks: a blueprint for Big Data sharing and healthcare analytics.

This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources.

Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets.

Access to experimental X-ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural biology community, we recently established a diffraction data publication system, the Structural Biology Data Grid (SBDG, data.sbgrid.org), to preserve primary experimental datasets supporting scientific publications. All datasets published through the SBDG are freely available to the research community under a public domain dedication license, with metadata compliant with the DataCite Schema (schema.datacite.org). A proof-of-concept study demonstrated community interest and utility. Publication of large datasets is a challenge shared by several fields, and the SBDG has begun collaborating with the Institute for Quantitative Social Science at Harvard University to extend the Dataverse (dataverse.org) open-source data repository system to structural biology datasets. Several extensions are necessary to support the size and metadata requirements for structural biology datasets. In this paper, we describe one such extension-functionality supporting preservation of file system structure within Dataverse-which is essential for both in-place computation and supporting non-HTTP data transfers.

A vision for collaborative training infrastructure for bioinformatics.

In biology, a missing link connecting data generation and data-driven discovery is the training that prepares researchers to effectively manage and analyze data. National and international cyberinfrastructure along with evolving private sector resources place biologists and students within reach of the tools needed for data-intensive biology, but training is still required to make effective use of them. In this concept paper, we review a number of opportunities and challenges that can inform the creation of a national bioinformatics training infrastructure capable of servicing the large number of emerging and existing life scientists. While college curricula are slower to adapt, grassroots startup-spirited organizations, such as Software and Data Carpentry, have made impressive inroads in training on the best practices of software use, development, and data analysis. Given the transformative potential of biology and medicine as full-fledged data sciences, more support is needed to organize, amplify, and assess these efforts and their impacts.

The Impact of Environment and Occupation on the Health and Safety of Active Duty Air Force Members: Database Development and De-Identification.

Preparing data for medical research can be challenging, detail oriented, and time consuming. Transcription errors, missing or nonsensical data, and records not applicable to the study population may hamper progress and, if unaddressed, can lead to erroneous conclusions. In addition, study data may be housed in multiple disparate databases and complex formats. Merging methods may be incomplete to obtain temporally synchronized data elements. We created a comprehensive database to explore the general hypothesis that environmental and occupational factors influence health outcomes and risk-taking behavior among active duty Air Force personnel. Several databases containing demographics, medical records, health survey responses, and safety incident reports were cleaned, validated, and linked to form a comprehensive, relational database. The final step involved removing and transforming personally identifiable information to form a Health Insurance Portability and Accountability Act compliant limited database. Initial data consisted of over 62.8 million records containing 221 variables. When completed, approximately 23.9 million clean and valid records with 214 variables remained. With a clean, robust database, future analysis aims to identify high-risk career fields for targeted interventions or uncover potential protective factors in low-risk career fields.

A Large-Scale Informatics Database to Advance Research and Discovery of the Effects of Mild Traumatic Brain Injury.

Clinical research advances in traumatic brain injury (TBI) and behavioral health have always been restricted by the quantity and quality of the data as well as the difficulty of collecting standardized clinical elements. Those barriers, together with the complexity of evaluating TBI, have resulted in serious challenges for clinicians, researchers, and organizations interested in analyzing the short- and long-term effects of TBI. In an effort to raise awareness about existing and cost-effective ways to collect clinical data within the Department of Defense, this article describes some of the steps taken to quickly build a large-scale informatics database to facilitate collection of standardized clinical data and obtain trends of the longitudinal outcomes of service members diagnosed with mild TBI. The database was built following the Defense of Health Agency guidelines and currently has millions of longitudinal clinical data points, Department of Defense-wide clinical data for service members diagnosed with mild TBI to support population studies, and multiple built-in analytical applications to enable interactive data exploration and analysis.

Informatics in neurocritical care: new ideas for Big Data.

PURPOSE OF REVIEW: Big data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care. RECENT FINDINGS: The first challenge in Big Data research will be the development of large, multicenter, and high-quality databases. These databases could be used to further investigate recent findings from mathematical models, developed in smaller datasets. Randomized clinical trials and Big Data research are complementary. Big Data research might be used to identify subgroups of patients that could benefit most from a certain intervention, or can be an alternative in areas where randomized clinical trials are not possible. SUMMARY: The processing and the analysis of the large amount of patient-related information stored in clinical databases is beyond normal human cognitive ability. Big Data research applications have the potential to discover new medical knowledge, and improve care in the neurointensive care unit.

The application of Big Data in medicine: current implications and future directions.

Since the mid 1980s, the world has experienced an unprecedented explosion in the capacity to produce, store, and communicate data, primarily in digital formats. Simultaneously, access to computing technologies in the form of the personal PC, smartphone, and other handheld devices has mirrored this growth. With these enhanced capabilities of data storage and rapid computation as well as real-time delivery of information via the internet, the average daily consumption of data by an individual has grown exponentially. Unbeknownst to many, Big Data has silently crept into our daily routines and, with continued development of cheap data storage and availability of smart devices both regionally and in developing countries, the influence of Big Data will continue to grow. This influence has also carried over to healthcare. This paper will provide an overview of Big Data, its benefits, potential pitfalls, and the projected impact on the future of medicine in general and cardiology in particular.

Computing Workflows for Biologists: A Roadmap.

Extremely large datasets have become routine in biology. However, performing a computational analysis of a large dataset can be overwhelming, especially for novices. Here, we present a step-by-step guide to computing workflows with the biologist end-user in mind. Starting from a foundation of sound data management practices, we make specific recommendations on how to approach and perform computational analyses of large datasets, with a view to enabling sound, reproducible biological research.

Drug development and nonclinical to clinical translational databases: past and current efforts.

The International Consortium for Innovation and Quality (IQ) in Pharmaceutical Development is a science-focused organization of pharmaceutical and biotechnology companies. The mission of the Preclinical Safety Leadership Group (DruSafe) of the IQ is to advance science-based standards for nonclinical development of pharmaceutical products and to promote high-quality and effective nonclinical safety testing that can enable human risk assessment. DruSafe is creating an industry-wide database to determine the accuracy with which the interpretation of nonclinical safety assessments in animal models correctly predicts human risk in the early clinical development of biopharmaceuticals. This initiative aligns with the 2011 Food and Drug Administration strategic plan to advance regulatory science and modernize toxicology to enhance product safety. Although similar in concept to the initial industry-wide concordance data set conducted by International Life Sciences Institute's Health and Environmental Sciences Institute (HESI/ILSI), the DruSafe database will proactively track concordance, include exposure data and large and small molecules, and will continue to expand with longer duration nonclinical and clinical study comparisons. The output from this work will help identify actual human and animal adverse event data to define both the reliability and the potential limitations of nonclinical data and testing paradigms in predicting human safety in phase 1 clinical trials.

Bringing biocuration to China.

Biocuration involves adding value to biomedical data by the processes of standardization, quality control and information transferring (also known as data annotation). It enhances data interoperability and consistency, and is critical in translating biomedical data into scientific discovery. Although China is becoming a leading scientific data producer, biocuration is still very new to the Chinese biomedical data community. In fact, there currently lacks an equivalent acknowledged word in Chinese for the word "curation". Here we propose its Chinese translation as (Pinyin) "shen bian", based on its implied meanings taken by biomedical data community. The 8th International Biocuration Conference to be held in China (http://biocuration2015.tilsi.org) next year bears the potential to raise the general awareness in China of the significant role of biocuration in scientific discovery. However, challenges are ahead in its implementation.