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big data analytics

Vineet M Arora
With the advent of electronic medical records (EMRs) fueling the rise of big data, the use of predictive analytics, machine learning, and artificial intelligence are touted as transformational tools to improve clinical care. While major investments are being made in using big data to transform health care delivery, little effort has been directed toward exploiting big data to improve graduate medical education (GME). Because our current system relies on faculty observations of competence, it is not unreasonable to ask whether big data in the form of clinical EMRs and other novel data sources can answer questions of importance in GME such as when is a resident ready for independent practice...
March 13, 2018: Academic Medicine: Journal of the Association of American Medical Colleges
Amaryllis Mavragani, Alexia Sampri, Karla Sypsa, Konstantinos P Tsagarakis
BACKGROUND: With the internet's penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma-a common respiratory disease-is present...
March 12, 2018: JMIR Public Health and Surveillance
GunHwan Ko, Pan-Gyu Kim, Jongcheol Yoon, Gukhee Han, Seong-Jin Park, Wangho Song, Byungwook Lee
BACKGROUND: While next-generation sequencing (NGS) costs have fallen in recent years, the cost and complexity of computation remain substantial obstacles to the use of NGS in bio-medical care and genomic research. The rapidly increasing amounts of data available from the new high-throughput methods have made data processing infeasible without automated pipelines. The integration of data and analytic resources into workflow systems provides a solution to the problem by simplifying the task of data analysis...
February 19, 2018: BMC Bioinformatics
Gonzalo Sirgo, Federico Esteban, Josep Gómez, Gerard Moreno, Alejandro Rodríguez, Lluis Blanch, Juan José Guardiola, Rafael Gracia, Lluis De Haro, María Bodí
BACKGROUND: Big data analytics promise insights into healthcare processes and management, improving outcomes while reducing costs. However, data quality is a major challenge for reliable results. Business process discovery techniques and an associated data model were used to develop data management tool, ICU-DaMa, for extracting variables essential for overseeing the quality of care in the intensive care unit (ICU). OBJECTIVE: To determine the feasibility of using ICU-DaMa to automatically extract variables for the minimum dataset and ICU quality indicators from the clinical information system (CIS)...
April 2018: International Journal of Medical Informatics
Charles S Mayo, Jean M Moran, Walter Bosch, Ying Xiao, Todd McNutt, Richard Popple, Jeff Michalski, Mary Feng, Lawrence B Marks, Clifton D Fuller, Ellen Yorke, Jatinder Palta, Peter E Gabriel, Andrea Molineu, Martha M Matuszak, Elizabeth Covington, Kathryn Masi, Susan L Richardson, Timothy Ritter, Tomasz Morgas, Stella Flampouri, Lakshmi Santanam, Joseph A Moore, Thomas G Purdie, Robert C Miller, Coen Hurkmans, Judy Adams, Qing-Rong Jackie Wu, Colleen J Fox, Ramon Alfredo Siochi, Norman L Brown, Wilko Verbakel, Yves Archambault, Steven J Chmura, Andre L Dekker, Don G Eagle, Thomas J Fitzgerald, Theodore Hong, Rishabh Kapoor, Beth Lansing, Shruti Jolly, Mary E Napolitano, James Percy, Mark S Rose, Salim Siddiqui, Christof Schadt, William E Simon, William L Straube, Sara T St James, Kenneth Ulin, Sue S Yom, Torunn I Yock
A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future...
March 15, 2018: International Journal of Radiation Oncology, Biology, Physics
Yuka Shiokawa, Yasuhiro Date, Jun Kikuchi
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general "linear" multivariate analysis alone limits interpretations due to "non-linear" variations in metabolic data from living organisms...
February 21, 2018: Scientific Reports
Valeria Catalani, Mariya Prilutskaya, Ahmed Al-Imam, Shanna Marrinan, Yasmine Elgharably, Mire Zloh, Giovanni Martinotti, Robert Chilcott, Ornella Corazza
Background : Octodrine is the trade name for Dimethylhexylamine (DMHA), a central nervous stimulant that increases the uptake of dopamine and noradrenaline. Originally developed as a nasal decongestant in the 1950's, it has recently been re-introduced on the market as a pre-workout and 'fat-burner' product but its use remains unregulated. Our work provides the first observational cross-sectional analytic study on Octodrine as a new drug trend and its associated harms after a gap spanning seven decades. Methods : A comprehensive multilingual assessment of literature, websites, drug fora and other online resources was carried out with no time restriction in English, German, Russian and Arabic...
February 20, 2018: Brain Sciences
Naim Mahroum, Nicola Luigi Bragazzi, Kassem Sharif, Vincenza Gianfredi, Daniele Nucci, Roberto Rosselli, Francesco Brigo, Mohammad Adawi, Howard Amital, Abdulla Watad
BACKGROUND: Technological advancements, such as patient-centered smartphone applications, have enabled to support self-management of the disease. Further, the accessibility to health information through the Internet has grown tremendously. This article aimed to investigate how big data can be useful to assess the impact of a celebrity's rheumatic disease on the public opinion. METHODS: Variable tools and statistical/computational approaches have been used, including massive data mining of Google Trends, Wikipedia, Twitter, and big data analytics...
February 14, 2018: Journal of Clinical Rheumatology: Practical Reports on Rheumatic & Musculoskeletal Diseases
Giorgos Dritsakis, Dimitris Kikidis, Nina Koloutsou, Louisa Murdin, Athanasios Bibas, Katherine Ploumidou, Ariane Laplante-Lévesque, Niels Henrik Pontoppidan, Doris-Eva Bamiou
INTRODUCTION: The holistic management of hearing loss (HL) requires an understanding of factors that predict hearing aid (HA) use and benefit beyond the acoustics of listening environments. Although several predictors have been identified, no study has explored the role of audiological, cognitive, behavioural and physiological data nor has any study collected real-time HA data. This study will collect 'big data', including retrospective HA logging data, prospective clinical data and real-time data via smart HAs, a mobile application and biosensors...
February 15, 2018: BMJ Open
Todd R McNutt, Michael Bowers, Sierra Cheng, Peijin Han, Xuan Hui, Joseph Moore, Scott Robertson, Charles Mayo, Ranh Voong, Harry Quon
The capture of high quality treatment and outcomes data is necessary in order to learn from our clinical experiences with big data analytics. In radiotherapy, there are several practical challenges to overcome. Practical aspects of data collection are discussed pointing to a need for a culture change in clinical practice to one that captures structured patient related data in routine care in a prospective manner. Radiation dosimetry and the contoured anatomy must also be captured routinely to represent the best estimate of delivered radiation...
February 15, 2018: Medical Physics
Hongbo Lin, Xun Tang, Peng Shen, Dudan Zhang, Jinguo Wu, Jingyi Zhang, Ping Lu, Yaqin Si, Pei Gao
INTRODUCTION: Data based on electronic health records (EHRs) are rich with individual-level longitudinal measurement information and are becoming an increasingly common data source for clinical risk prediction worldwide. However, few EHR-based cohort studies are available in China. Harnessing EHRs for research requires a full understanding of data linkages, management, and data quality in large data sets, which presents unique analytical opportunities and challenges. The purpose of this study is to provide a framework to establish a uniquely integrated EHR database in China for scientific research...
February 12, 2018: BMJ Open
Dong D Wang, Frank B Hu
Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations to one or a combination of an individual's genetic background, metabolic profile, and environmental exposures. Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals' variability in response to dietary interventions...
February 9, 2018: Lancet Diabetes & Endocrinology
Vineet K Raghu, Xiaoyu Ge, Panos K Chrysanthis, Panayiotis V Benos
The exponential growth of high dimensional biological data has led to a rapid increase in demand for automated approaches for knowledge production. Existing methods rely on two general approaches to address this challenge: 1) the Theory-driven approach, which utilizes prior accumulated knowledge, and 2) the Data-driven approach, which solely utilizes the data to deduce scientific knowledge. Both of these approaches alone suffer from bias toward past/present knowledge, as they fail to incorporate all of the current knowledge that is available to make new discoveries...
April 2017: Proceedings
James W Baurley, Christopher S McMahan, Carolyn M Ervin, Bens Pardamean, Andrew W Bergen
There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types...
February 3, 2018: Trends in Molecular Medicine
Patricia Balthazar, Peter Harri, Adam Prater, Nabile M Safdar
The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The increasing use of big data and artificial intelligence techniques demands a re-examination of these principles in light of the potential issues surrounding privacy, confidentiality, data ownership, informed consent, epistemology, and inequities. Patients have strong opinions about these issues. Radiologists have a fiduciary responsibility to protect the interest of their patients...
February 2, 2018: Journal of the American College of Radiology: JACR
Scott M Sutherland, Stuart L Goldstein, Sean M Bagshaw
The recognition of a standardized, consensus definition for acute kidney injury (AKI) has been an important milestone in critical care nephrology, which has facilitated innovation in prevention, quality of care, and outcomes research among the growing population of hospitalized patients susceptible to AKI. Concomitantly, there have been substantial advances in "big data" technologies in medicine, including electronic health records (EHR), data registries and repositories, and data management and analytic methodologies...
2018: Contributions to Nephrology
Daniel A Hashimoto, Guy Rosman, Daniela Rus, Ozanan R Meireles
OBJECTIVE: The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. SUMMARY BACKGROUND DATA: AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers...
January 31, 2018: Annals of Surgery
Anna Marie Williams, Yong Liu, Kevin R Regner, Fabrice Jotterand, Pengyuan Liu, Mingyu Liang
Big data is a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning towards machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records...
January 26, 2018: Physiological Genomics
Harry Hemingway, Folkert W Asselbergs, John Danesh, Richard Dobson, Nikolaos Maniadakis, Aldo Maggioni, Ghislaine Jm van Thiel, Maureen Cronin, Gunnar Brobert, Panos Vardas, Stefan D Anker, Diederick E Grobbee, Spiros Denaxas
Aims: Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. Methods and results: We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium...
August 29, 2017: European Heart Journal
Ava L Liberman, David E Newman-Toker
BACKGROUND: The public health burden associated with diagnostic errors is likely enormous, with some estimates suggesting millions of individuals are harmed each year in the USA, and presumably many more worldwide. According to the US National Academy of Medicine, improving diagnosis in healthcare is now considered 'a moral, professional, and public health imperative.' Unfortunately, well-established, valid and readily available operational measures of diagnostic performance and misdiagnosis-related harms are lacking, hampering progress...
January 22, 2018: BMJ Quality & Safety
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