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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/29149552/predicting-nano-bio-interactions-by-integrating-nanoparticle-libraries-and-quantitative-nanostructure-activity-relationship-modeling
#3
Wenyi Wang, Alexander Sedykh, Hainan Sun, Linlin Zhao, Daniel P Russo, Hongyu Zhou, Bing Yan, Hao Zhu
The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: 1) the lack of high quality comprehensive data for computational modeling, and 2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities...
November 17, 2017: ACS Nano
https://www.readbyqxmd.com/read/29149267/taiwan-biobank-making-cross-database-convergence-possible-in-the-big-data-era
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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/29132334/is-perfect-good-dimensions-of-perfectionism-in-newly-admitted-medical-students
#14
Helen Seeliger, Sigrid Harendza
BACKGROUND: Society expects physicians to perform perfectly but high levels of perfectionism are associated with symptoms of distress in medical students. This study investigated whether medical students admitted to medical school by different selection criteria differ in the occurrence of perfectionism. METHODS: Newly enrolled undergraduate medical students (n = 358) filled out the following instruments: Multidimensional Perfectionism Scale (MPS-H), Multidimensional Perfectionism Scale (MPS-F), Big Five Inventory (BFI-10), General Self-Efficacy Scale (GSE), Patient Health Questionnaire 9 (PHQ-9), and Generalized Anxiety Disorder 7 (GAD-7)...
November 13, 2017: BMC Medical Education
https://www.readbyqxmd.com/read/29131819/correction-unmet-needs-for-analyzing-biological-big-data-a-survey-of-704-nsf-principal-investigators
#15
(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/29131683/persistence-of-non-o157-shiga-toxin-producing-escherichia-coli-in-dairy-compost-during-storage
#16
Hongye Wang, Muthu Dharmasena, Zhao Chen, Xiuping Jiang
Dairy compost with 20, 30, or 40% moisture content (MC) was inoculated with a mixture of six non-O157 Shiga toxin-producing Escherichia coli (STEC) serovars at a final concentration of 5.1 log CFU/g and then stored at 22 and 4°C for 125 days. Six storage conditions-4°C and 20% MC, 4°C and 30% MC, 4°C and 40% MC, 22°C and 20% MC, 22°C and 30% MC, and 22°C and 40% MC-were investigated for the persistence of non-O157 STEC in the dairy compost. During the entire storage, fluctuations in indigenous mesophilic bacterial levels were observed within the first 28 days of storage...
November 14, 2017: Journal of Food Protection
https://www.readbyqxmd.com/read/29130757/international-barcode-of-life-focus-on-big-biodiversity-in-south-africa
#17
Sarah J Adamowicz, Peter M Hollingsworth, Sujeevan Ratnasingham, Michelle van der Bank
Participants in the 7th International Barcode of Life Conference (Kruger National Park, South Africa, 20-24 November 2017) share the latest findings in DNA barcoding research and its increasingly diversified applications. Here, we review prevailing trends synthesized from among 429 invited and contributed abstracts, which are collated in this open-access special issue of Genome. Hosted for the first time on the African continent, the 7th Conference places special emphasis on the evolutionary origins, biogeography, and conservation of African flora and fauna...
November 2017: Genome Génome / Conseil National de Recherches Canada
https://www.readbyqxmd.com/read/29129969/statistical-contributions-to-bioinformatics-design-modeling-structure-learning-and-integration
#18
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
#19
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
#20
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
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