Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, ...blockchain represents a data architecture, whose application goes far beyond Bitcoin - the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as 'revolutionary' and 'disruptive', is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is 'fit-for-purpose' is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide ...range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.This book provides the tools needed to thrive in today's big data world. The author demonstrates how to leverage a company's existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will "learn data mining by doing data mining". By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining.The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and VisualizationOffers extensive coverage of the R statistical programming languageContains 280 end-of-chapter exercisesIncludes a companion website with further resources for all readers, and Powerpoint slides, a solutions manual, and suggested projects for instructors who adopt the book
In the last few decades, precision medicine became a highly weighted subject as the risk of side effects grew with the increasing drug use. The mass of evolving pharmacogenetic data invites to ...establish a preemptive genotyping approach and the integration of those molecular data into the workflow of an existing hospital information system to improve the safety of drug therapy. The newly introduced software module GraphSAW2-DWHBuilder creates the graph-based data warehouse. For that, molecular data from various sources is assembled in a graph database. Furthermore, an update strategy was developed to ensure that only the latest version of the data is used. To eliminate redundancies and to guarantee efficient querying, a mapper strategy was established. Another new module named GraphSAW2-Check is embedded in an existing drug therapy safety workflow used in hospitals. It contains methods for the pharmacogenetic check, which access the molecular data warehouse of the GraphSAW2-DWHBuilder and execute queries on the graph. These queries contain the drugs and the molecular data of interest and search for connections that represent toxic associations between them. If a connection is found, a warning will be issued to the medical practitioner, who can then reconsider their prescription. The results reveal the added value of molecular data integration to the drug therapy safety.
We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine‐learning algorithms. Slated to take effect as law across the ...European Union in 2018, it will place restrictions on automated individual decision making (that is, algorithms that make decisions based on user‐level predictors) that “significantly affect” users. When put into practice, the law may also effectively create a right to explanation, whereby a user can ask for an explanation of an algorithmic decision that significantly affects them. We argue that while this law may pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks that avoid discrimination and enable explanation.
Abstract
The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in ...higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
Abstract
The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides a suite of database resources in support of worldwide research activities in both ...academia and industry. With the vast amounts of multi-omics data generated at unprecedented scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. Resources with significant updates in the past year include BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Science Wikis (a catalog of biological knowledge wikis for community annotations) and IC4R (Information Commons for Rice). Newly released resources include EWAS Atlas (a knowledgebase of epigenome-wide association studies), iDog (an integrated omics data resource for dog) and RNA editing resources (for editome-disease associations and plant RNA editosome, respectively). To promote biodiversity and health big data sharing around the world, the Open Biodiversity and Health Big Data (BHBD) initiative is introduced. All of these resources are publicly accessible at http://bigd.big.ac.cn.
The international conference on Data Warehousing and Knowledge Discovery (DaWaK) has become a pivotal place to exchange experiences and knowledge among researchers and practitioners in big data ...analytics. The conference has been essential to data warehousing and data analytics for the last 21 years (1999–2019). This study explored the knowledge structure embedded in the DaWaK Conference papers and examined the research trends over time. It also analyzed the performance of published papers, authors, and their affiliations and countries and visualized a collaboration network in DaWaK. We applied several text mining techniques, including co-word analysis, topic modeling, co-author network analysis, and network visualization. The study’s findings indicate that the core topics are data mining techniques, algorithm performance, and information systems. The popular topic trends are associated with database encryption, whereas the topics related to online analytical processing (OLAP) technology are in decline. The research metrics results demonstrate that the DaWaK papers were cited 6,262 times, with an h-index of 34 for the 722 DaWaK papers. The article titled “Outlier Detection Using Replicator Neural Networks” reached the most citations (177), and the most productive author was Bellatreche, Ladjel (15 papers). Nanyang Technological University is the most frequently mentioned as the author’s affiliation, the United States is the country with the largest number of authors, and the National Science Foundation was the largest funding agency that supported the DaWaK researchers. Moreover, the authorship network of Bellatreche, Ladjel is the largest collaboration network in the DaWaK scholar community. The outcomes of this study would be beneficial for comprehending the knowledge in data warehousing and the relevant cross-disciplinary areas of research and collaboration networks in this field.
Clinical publications use mortality as a hard end point. It is unknown how many patient deaths are under-reported in institutional databases. The objective of this study was to query mortality in our ...patient cohort from our data warehouse and compare these deaths to those identified in different databases.
We passed the first/last name and date of birth of 134 patients through online mortality search engines (Find a Grave Index, US Cemetery and Funeral Home Collection, etc.) to assess their ability to capture patient deaths and compared that to deaths recorded from our institutional data warehouse.
Our institutional data warehouse found approximately one-third of the total patient mortalities. After the Social Security Death Index, we found that the Find a Grave Index captured the most mortalities missed by the institutional data warehouse. These results highlight the advantages of incorporating readily available search engines into institutional data warehouses for the accurate collection of patient mortalities, particularly those that occur outside of index operative admission.
The incorporation of the mortality search engines significantly augmented the capture of patient deaths. Our approach may be useful for tailored patient outreach and reporting mortalities with institutional data.
Little is known about how use patterns of medications for opioid use disorder (MOUDs) evolve from pre-incarceration to post-incarceration among incarcerated individuals with opioid use disorder. This ...article describes pre- and post-incarceration MOUD receipt during a period when naltrexone was the only type of MOUD offered in a state prison system, the Massachusetts Department of Correction (MADOC).
A retrospective cohort study of individuals with opioid use disorder who had an incarceration episode in MADOC during January 2015 to March 2019. The data source was the Massachusetts Public Health Data Warehouse, a multi-sector data platform that links individual-level data from multiple statewide datasets. We described patterns of MOUD receipt during the four weeks prior to and after an incarceration episode. Multivariable logistic regression models characterized predictors of post-incarceration MOUD receipt.
In the male sample (n=691 incarcerations), from the pre- to post-incarceration periods, receipt of buprenorphine increased (14.3 % to 18.3 %), naltrexone increased (5.0 % to 10.5 %), and methadone decreased (4.7 % to 1.7 %). Similarly, in the female sample (n=892 incarcerations), from the pre- to post-incarceration periods, receipt of buprenorphine increased (10.3 % to 12.3 %, naltrexone increased (4.5 % to 9.3 %), and methadone decreased (5.0 % to 2.9 %). Much of the post-release naltrexone receipt occurred among participants in MADOC’s pre-release naltrexone program.
MOUD receipt was low but increased slightly in the post-incarceration period. This change was driven by increases in buprenorphine and naltrexone and despite decreases in methadone.
•Medications for opioid use disorder treatment were uncommon before and after prison.•Buprenorphine and naltrexone increased post-incarceration, but methadone decreased.•Massachusetts Public Health Data Warehouse linked incarceration and health data.•White persons received more MOUD than Black persons before and after prison stays.
•We propose that resource complementarity gives rise to a higher-order, strategic firm asset we refer to as “big data resources.”•The study results reveal that big data resources primarily improve ...firm performance by enhancing the market-directed capabilities.•Big data resources account for 13% of the variance in the performance of firms with a differentiation strategy and 3% for cost-leadership.
Despite being the object of much interest, deep insights regarding why and when investments in big data resources enhance firm performance are lacking in the literature. Building on the resource-based view (RBV) and data provided by 301 senior marketing managers, this study reveals that big data resources primarily improve firm performance by enhancing the market-directed capabilities of the firm. In addition, the data indicate that firms pursuing a differentiation rather than cost-leadership strategy gain most from big data resource investments and that such resources account for 13 % of the variance in the performance of firms pursuing a differentiation strategy.
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