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Big data medicine

Peter Savadjiev, Jaron Chong, Anthony Dohan, Maria Vakalopoulou, Caroline Reinhold, Nikos Paragios, Benoit Gallix
The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments...
August 13, 2018: European Radiology
James Brogan, Immanuel Baskaran, Navin Ramachandran
The on-demand digital healthcare ecosystem is on the near horizon. It has the potential to extract a wealth of information from "big data" collected at the population level, to enhance preventive and precision medicine at the patient level. This may improve efficiency and quality while decreasing cost of healthcare delivered by professionals. However, there are still security and privacy issues that need to be addressed before algorithms, data, and models can be mobilized safely at scale. In this paper we discuss how distributed ledger technologies can play a key role in advancing electronic health, by ensuring authenticity and integrity of data generated by wearable and embedded devices...
2018: Computational and Structural Biotechnology Journal
Amelia Fiske, Alena Buyx, Barbara Prainsack
Health care is increasingly data-driven. Concurrently, there are growing concerns that health professionals lack the time and training to guide patients through the growing medical "data jungle." In the age of big data, ever wider domains of people's lives are "datafied," which renders ever more information-at least in principle-usable for health care purposes. Turning data into meaningful information for clinical practice-and deciding what data or information should not be used for this purpose-requires a significant amount of time, resources, and skill...
August 7, 2018: Academic Medicine: Journal of the Association of American Medical Colleges
Nabil M Elkassabany, Stavros G Memtsoudis, Edward R Mariano
Demonstrating value added to patients' experience through regional anesthesiology and acute pain medicine is critical. Evidence supporting improved outcomes can be derived from prospective studies or retrospective cohort studies. Population-based studies relying on existing clinical and administrative databases are helpful when an outcome is rare and detecting a change would require studying large numbers of patients. This article discusses the effect of regional anesthesiology and acute pain medicine interventions on mortality and morbidity, infection rate, cancer recurrence, inpatient falls, local anesthetic systemic toxicity, persistent postsurgical pain, and health care costs...
September 2018: Anesthesiology Clinics
Ala'a A S Al-Johani, Samia Sabor, Sami A R Aldubai
Background: Rapid and right intervention of parents can limit disability and increase the chances of survival of the injured child and make a big difference in the outcome. Objectives: The objective of this study is to assess the knowledge and practice of first aid among parents attending PHCs in Al-Madinah city, 2017. Methods: A cross-sectional study was conducted in governmental primary healthcare centers in Al-Madinah Al-Munawwarah city (Saudi Arabia) during the year 2017...
March 2018: Journal of Family Medicine and Primary Care
Adrienne N Cobb, Andrew J Benjamin, Erich S Huang, Paul C Kuo
The term big data has been popularized over the past decade and is often used to refer to data sets that are too large or complex to be analyzed by traditional means. Although the term has been utilized for some time in business and engineering, the concept of big data is relatively new to medicine. The reception from the medical community has been mixed; however, the widespread utilization of electronic health records in the United States, the creation of large clinical data sets and national registries that capture information on numerous vectors affecting healthcare delivery and patient outcomes, and the sequencing of the human genome are all opportunities to leverage big data...
July 27, 2018: Surgery
Amparo Alonso-Betanzos, Verónica Bolón-Canedo
Medicine will experience many changes in the coming years because the so-called "medicine of the future" will be increasingly proactive, featuring four basic elements: predictive, personalized, preventive, and participatory. Drivers for these changes include the digitization of data in medicine and the availability of computational tools that deal with massive volumes of data. Thus, the need to apply machine-learning methods to medicine has increased dramatically in recent years while facing challenges related to an unprecedented large number of clinically relevant features and highly specific diagnostic tests...
2018: Advances in Experimental Medicine and Biology
M Jäger, C Mayer, H Hefter, M Siebler, A Kecskeméthy
The digitalization in medicine has led to almost universal availability of information to different healthcare professionals and accelerated clinical pathways. Fast-track concepts and short hospital stays require intelligent and practicable systems in preventive and rehabilitation medicine. This includes optimization of movement analysis by innovative tools such as detectors sensing skin movements, portable feedback systems for monitoring, robot-assisted devices, and prevention programs based on reliable data...
July 23, 2018: Der Orthopäde
Sumeet Kumar, Navneesh Yadav, Sanjay Pandey, B K Thelma
Neurodegenerative diseases constitute a large proportion of disorders in elderly, majority being sporadic in occurrence with ∼5-10% familial. A strong genetic component underlies the Mendelian forms but nongenetic factors together with genetic vulnerability contributes to the complex sporadic forms. Several gene discoveries in the familial forms have provided novel insights into the pathogenesis of neurodegeneration with implications for treatment. Conversely, findings from genetic dissection of the sporadic forms, despite large genomewide association studies and more recently whole exome and whole genome sequencing, have been limited...
July 2018: Journal of Genetics
Kate A Timmins, Mark A Green, Duncan Radley, Michelle A Morris, Jamie Pearce
There has been growing interest in the potential of 'big data' to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are 'found' (as opposed to 'made'), in that they have been collected for non-research purposes, but could include valuable information for research. The aim of this paper is to review the contribution of 'found' data to obesity research to date, and describe the benefits and challenges encountered...
July 18, 2018: International Journal of Obesity: Journal of the International Association for the Study of Obesity
Zhengbo Zhang, Wanguo Xue, Desen Cao, Tanshi Li
A detailed, high-scale clinical data can be generated in the process of diagnosis and treatment of emergency critically ill patients. The integration and analysis and utilization of these data are of great value for improving the treatment level and efficiency and developing the data-driven clinical assistant decision support. China has large volume of health information resources, however, the construction of healthcare databases and subsequent secondary analysis has just started. With the effort of the Chinese PLA General Hospital in building an emergency database and promoting data sharing, the first emergency database was published in China and a health Datathon was organized utilizing this database, providing experience for clinical data integration, database construction, cross-disciplinary collaboration and data sharing...
June 2018: Zhonghua Wei Zhong Bing Ji Jiu Yi Xue
Mustafa Sertbas, Kutlu O Ulgen
Neurology research and clinical practice are transforming toward postgenomics integrative biology. One such example is the study of human brain metabolism that is highly sophisticated due to reactions occurring in and between the astrocytes and neurons. Because of the inherent difficulty of performing experimental studies in human brain, metabolic network modeling has grown in importance to decipher the contribution of brain metabolite kinetics to human health and disease. Multiomics system science-driven metabolic models, using genome-scale and transcriptomics Big Data, offer the promise of new insights on metabolic networks in human brain...
July 2018: Omics: a Journal of Integrative Biology
B S Gerendas, S M Waldstein, U Schmidt-Erfurth
BACKGROUND: Modern retinal imaging creates gigantic amounts of data (big data) of anatomic information. At the same time patient numbers and interventions are increasing exponentially. OBJECTIVE: Introduction of artificial intelligence (AI) for optimization of personalized therapy and diagnosis. MATERIAL AND METHODS: Deep learning was introduced for automated segmentation and recognition of risk factors and activity levels in retinal diseases...
July 6, 2018: Der Ophthalmologe: Zeitschrift der Deutschen Ophthalmologischen Gesellschaft
Warren M Perry, Rubayet Hossain, Richard A Taylor
BACKGROUND: The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algorithms, however, is dependent on data being present and entered prior to, or at the point of, CDSS deployment. Our aim was to determine the feasibility of automating CDSSs within electronic health records (EHRs) by investigating the timing, data categorization, and completeness of documentation of their individual components of two common Clinical Decision Rules (CDRs) in the Emergency Department...
July 3, 2018: BMC Emergency Medicine
Nathan C Wong, Cameron Lam, Lisa Patterson, Bobby Shayegan
OBJECTIVES: To train and compare machine learning algorithms to traditional regression analysis for the prediction of early biochemical recurrence following robotic prostatectomy. Machine learning allows for the analysis and interpretation of "big data" in a semi-automated and adaptive fashion. Predictive algorithms can arm clinicians with knowledge that can provide personalized medicine to patient care. SUBJECTS AND METHODS: A prospectively collected dataset of 338 patients who underwent robotic prostatectomy for localized prostate cancer was examined...
July 3, 2018: BJU International
Judith Pratt, Jeremy Hall
The search for biomarkers to aid in the diagnosis and prognosis of psychiatric conditions and predict response to treatment is a focus of twenty-first century medicine. The current lack of biomarkers in routine use is attributable in part to the existing way mental health conditions are diagnosed, being based upon descriptions of symptoms rather than causal biological evidence. New ways of conceptualizing mental health disorders together with the enormous advances in genetic, epidemiological, and neuroscience research are informing the brain circuits and physiological mechanisms underpinning behavioural constructs that cut across current diagnostic DSM-5 categories...
July 3, 2018: Current Topics in Behavioral Neurosciences
Angela Ballantyne
Being asked to write about the ethics of big data is a bit like being asked to write about the ethics of life. Big data is now integral to so many aspects of our daily lives-communication, social interaction, medicine, access to government services, shopping, and navigation. Given this diversity, there is no one-size-fits-all framework for how to ethically manage your data. With that in mind, I present seven ethical values for responsible data use.
July 1, 2018: GigaScience
Priti Nagdeve, Asha Shetty
BACKGROUND: Mysodelle is a 200 mcg misoprostol, vaginal delivery system. It is a PGE1 analogue and accepted as a method of IOL by Scottish medicine consortium in 2014 (Medicines Health and Regulatory Authority; Wing et al., 2013 ). AIMS: The main objective of this project was to determine efficacy of Mysodelle with regards to time interval between insertions to delivery. We also studied the safety profile of Mysodelle with regards to operative delivery rates, foetal concerns and incidence of hyperstimulation...
July 2018: Journal of Obstetrics and Gynaecology: the Journal of the Institute of Obstetrics and Gynaecology
Hui-Qi See, Jin-Ning Chan, Shu-Jin Ling, Shang-Cheng Gan, Chee-Onn Leong, Chun-Wai Mai
Big data is anticipated to have large implications in clinical pharmacy, in view of its potential in enhancing precision medicine and to avoid medication error. However, it is equally debatable since such a powerful tool may also disrupt the need of pharmacist in healthcare industry. In this article, we commented the contribution of Big Data in various aspects of clinical pharmacy including advancing pharmaceutical care service, optimising drug supplies, managing clinical trials, and strengthening pharmacovigilance...
2018: Journal of Pharmacy & Pharmaceutical Sciences: a Publication of the Canadian Society for Pharmaceutical Sciences
Kevin F Erickson, Samaya Qureshi, Wolfgang C Winkelmayer
Rapid growth in electronic communications and digitalization, combined with advances in data management, analysis, and storage, have led to an era of "Big Data." The Social Security Amendments of 1972 turned end-stage renal disease (ESRD) care into a single-payer system for most patients requiring dialysis in the United States. As a result, there are few areas of medicine that have been as influenced by Big Data as dialysis care, for which Medicare's large administrative data sets have had a central role in the evaluation and development of public policy for several decades...
June 16, 2018: American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation
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