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https://www.readbyqxmd.com/read/29150571/using-big-data-to-improve-patient-safety
#1
(no author information available yet)
No abstract text is available yet for this article.
November 18, 2017: Veterinary Record
https://www.readbyqxmd.com/read/29150191/-big-data-generalities-and-integration-in-radiotherapy
#2
C Le Fèvre, L Poty, G Noël
The many advances in data collection computing systems (data collection, database, storage), diagnostic and therapeutic possibilities are responsible for an increase and a diversification of available data. Big data offers the capacities, in the field of health, to accelerate the discoveries and to optimize the management of patients by combining a large volume of data and the creation of therapeutic models. In radiotherapy, the development of big data is attractive because data are very numerous et heterogeneous (demographics, radiomics, genomics, radiogenomics, etc...
November 14, 2017: Cancer Radiothérapie: Journal de la Société Française de Radiothérapie Oncologique
https://www.readbyqxmd.com/read/29149267/taiwan-biobank-making-cross-database-convergence-possible-in-the-big-data-era
#3
Jui-Chu Lin, Chien-Te Fan, Chia-Cheng Liao, Yao-Sheng Chen
The Taiwan Biobank (TWB) is a biomedical research database of biopsy data from 200,000 participants. Access to this database has been granted to research communities taking part in the development of precision medicines; however, this has raised issues surrounding TWB's access to electronic medical records (EMR). The Personal Data Protection Act of Taiwan restricts access to EMR for purposes not covered by patients' original consent. This commentary explores possible legal solutions to help ensure that the access TWB has to EMR abides with legal obligations, and with governance frameworks associated with ethical, legal and social implications...
November 15, 2017: GigaScience
https://www.readbyqxmd.com/read/29148112/big-data-and-data-science-in-healthcare-what-nurses-and-midwives-need-to-know
#4
EDITORIAL
Siobhan O'Connor
The evolution of technology in contemporary society has been accelerating in recent decades, with smaller, more interconnected hardware devices and software applications becoming the norm. As desktop computing paved the way for mobile platforms, which are now transitioning to wearable devices and other sensors, it is inevitable these electronic tools will advance into the realm of nanotechnology and biotechnology in the years to come. As the proliferation of information and communication technology continues it has led to a tsunami of digital data (Bates et al, 2014), which is being collected on many aspects of life...
November 17, 2017: Journal of Clinical Nursing
https://www.readbyqxmd.com/read/29147562/cognitive-computing-and-escience-in-health-and-life-science-research-artificial-intelligence-and-obesity-intervention-programs
#5
REVIEW
Thomas Marshall, Tiffiany Champagne-Langabeer, Darla Castelli, Deanna Hoelscher
Objective: To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. Methods: The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29147518/machine-learning-molecular-dynamics-for-the-simulation-of-infrared-spectra
#6
Michael Gastegger, Jörg Behler, Philipp Marquetand
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated...
October 1, 2017: Chemical Science
https://www.readbyqxmd.com/read/29146206/using-big-data-for-non-communicable-disease-surveillance
#7
Ran D Balicer, Miguel Luengo-Oroz, Chandra Cohen-Stavi, Enrique Loyola, Frederiek Mantingh, Liudmyla Romanoff, Gauden Galea
No abstract text is available yet for this article.
November 13, 2017: Lancet Diabetes & Endocrinology
https://www.readbyqxmd.com/read/29142631/mortality-and-epidemiology-in-256-cases-of-pediatric-traumatic-brain-injury-korean-neuro-trauma-data-bank-system-kntdbs-2010-2014
#8
Hee-Won Jeong, Seung-Won Choi, Jin-Young Youm, Jeong-Wook Lim, Hyon-Jo Kwon, Shi-Hun Song
Objective: Among pediatric injury, brain injury is a leading cause of death and disability. To improve outcomes, many developed countries built neurotrauma databank (NTDB) system but there was not established nationwide coverage NTDB until 2009 and there have been few studies on pediatric traumatic head injury (THI) patients in Korea. Therefore, we analyzed epidemiology and outcome from the big data of pediatric THI. Methods: We collected data on pediatric patients from 23 university hospitals including 9 regional trauma centers from 2010 to 2014 and analyzed their clinical factors (sex, age, initial Glasgow coma scale, cause and mechanism of head injury, presence of surgery)...
November 2017: Journal of Korean Neurosurgical Society
https://www.readbyqxmd.com/read/29140477/when-can-the-child-speak-for-herself-the-limits-of-parental-consent-in-data-protection-law-for-health-research
#9
Mark J Taylor, Edward S Dove, Graeme Laurie, David Townend
Draft regulatory guidance suggests that if the processing of a child's personal data begins with the consent of a parent, then there is a need to find and defend an enduring consent through the child's growing capacity and on to their maturity. We consider the implications for health research of the UK Information Commissioner's Office's (ICO) suggestion that the relevant test for maturity is the Gillick test, originally developed in the context of medical treatment. Noting the significance of the welfare principle to this test, we examine the implications for the responsibilities of a parent to act as proxy for their child...
November 13, 2017: Medical Law Review
https://www.readbyqxmd.com/read/29140462/data-portal-for-the-library-of-integrated-network-based-cellular-signatures-lincs-program-integrated-access-to-diverse-large-scale-cellular-perturbation-response-data
#10
Amar Koleti, Raymond Terryn, Vasileios Stathias, Caty Chung, Daniel J Cooper, John P Turner, Dušica Vidovic, Michele Forlin, Tanya T Kelley, Alessandro D'Urso, Bryce K Allen, Denis Torre, Kathleen M Jagodnik, Lily Wang, Sherry L Jenkins, Christopher Mader, Wen Niu, Mehdi Fazel, Naim Mahi, Marcin Pilarczyk, Nicholas Clark, Behrouz Shamsaei, Jarek Meller, Juozas Vasiliauskas, John Reichard, Mario Medvedovic, Avi Ma'ayan, Ajay Pillai, Stephan C Schürer
The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses...
November 13, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/29135771/detecting-lung-and-colorectal-cancer-recurrence-using-structured-clinical-administrative-data-to-enable-outcomes-research-and-population-health-management
#11
Michael J Hassett, Hajime Uno, Angel M Cronin, Nikki M Carroll, Mark C Hornbrook, Debra Ritzwoller
INTRODUCTION: Recurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations. METHODS: We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and colorectal cancer using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the Cancer Care Outcomes Research and Surveillance study, and the Cancer Research Network Virtual Data Warehouse linked to registry data...
December 2017: Medical Care
https://www.readbyqxmd.com/read/29134624/challenges-for-training-translational-researchers-in-the-era-of-ubiquitous-data
#12
Russ B Altman
Our ability to collect data at every stage of the translational pipeline creates great opportunities for formulating hypotheses both "upstream" (towards clinical implementation) and "downstream" (back to basic discovery). Translational researchers therefore must integrate information at multiple scales to both generate and test hypotheses-to some extent they must all be comfortable with the basics of "big data" analyses. This increased focus on data-driven science requires an understanding of basic experimental and clinical data collection-understanding that likely cannot efficiently be gathered through traditional apprenticeship models...
November 14, 2017: Clinical Pharmacology and Therapeutics
https://www.readbyqxmd.com/read/29131819/correction-unmet-needs-for-analyzing-biological-big-data-a-survey-of-704-nsf-principal-investigators
#13
(no author information available yet)
[This corrects the article DOI: 10.1371/journal.pcbi.1005755.].
November 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29129969/statistical-contributions-to-bioinformatics-design-modeling-structure-learning-and-integration
#14
Jeffrey S Morris, Veerabhadran Baladandayuthapani
The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of Bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain...
2017: Statistical Modelling
https://www.readbyqxmd.com/read/29126825/artificial-intelligence-in-medical-practice-the-question-to-the-answer
#15
REVIEW
D Douglas Miller, Eric W Brown
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society - forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence...
November 7, 2017: American Journal of Medicine
https://www.readbyqxmd.com/read/29126253/enabling-phenotypic-big-data-with-phenorm
#16
Sheng Yu, Yumeng Ma, Jessica Gronsbell, Tianrun Cai, Ashwin N Ananthakrishnan, Vivian S Gainer, Susanne E Churchill, Peter Szolovits, Shawn N Murphy, Isaac S Kohane, Katherine P Liao, Tianxi Cai
Objective: Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck...
November 3, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29124046/the-analysis-of-a-diet-for-the-human-being-and-the-companion-animal-using-big-data-in-2016
#17
Eun-Jin Jung, Young-Suk Kim, Jung-Wa Choi, Hye Won Kang, Un-Jae Chang
The purpose of this study was to investigate the diet tendencies of human and companion animals using big data analysis. The keyword data of human diet and companion animals' diet were collected from the portal site Naver from January 1, 2016 until December 31, 2016 and collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis and seasonality analysis. In terms of human, the word exercise had the highest frequency through simple frequency analysis, whereas diet menu most frequently appeared in the N-gram analysis...
October 2017: Clinical Nutrition Research
https://www.readbyqxmd.com/read/29123643/coinstac-decentralizing-the-future-of-brain-imaging-analysis
#18
Jing Ming, Eric Verner, Anand Sarwate, Ross Kelly, Cory Reed, Torran Kahleck, Rogers Silva, Sandeep Panta, Jessica Turner, Sergey Plis, Vince Calhoun
In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016...
2017: F1000Research
https://www.readbyqxmd.com/read/29119766/-big-data-versus-randomized-clinical-trials-nobody-benefits-from-the-contrast
#19
Antonio Addis
No abstract text is available yet for this article.
September 2017: Epidemiologia e Prevenzione
https://www.readbyqxmd.com/read/29119460/personal-genomic-testing-genetic-inheritance-and-uncertainty
#20
Paul H Mason
The case outlined below is the basis for the In That Case section of the "Ethics and Epistemology of Big Data" symposium. Jordan receives reports from two separate personal genomic tests that provide intriguing data about ancestry and worrying but ambiguous data about the potential risk of developing Alzheimer's disease. What began as a personal curiosity about genetic inheritance turns into an alarming situation of medical uncertainty. Questions about Jordan's family tree are overshadowed by even more questions about Alzheimer's disease and healthy ageing...
November 8, 2017: Journal of Bioethical Inquiry
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