Hasil untuk "Medicine"

Jenni A. M. Sidey-Gibbons, C. Sidey-Gibbons

BackgroundFollowing visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.MethodsWe demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples.We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment.ResultsThe trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble.ConclusionsWe use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.

K. S. Egorova, E. Gordeev, V. Ananikov

Ionic liquids are remarkable chemical compounds, which find applications in many areas of modern science. Because of their highly tunable nature and exceptional properties, ionic liquids have become essential players in the fields of synthesis and catalysis, extraction, electrochemistry, analytics, biotechnology, etc. Apart from physical and chemical features of ionic liquids, their high biological activity has been attracting significant attention from biochemists, ecologists, and medical scientists. This Review is dedicated to biological activities of ionic liquids, with a special emphasis on their potential employment in pharmaceutics and medicine. The accumulated data on the biological activity of ionic liquids, including their antimicrobial and cytotoxic properties, are discussed in view of possible applications in drug synthesis and drug delivery systems. Dedicated attention is given to a novel active pharmaceutical ingredient-ionic liquid (API-IL) concept, which suggests using traditional drugs in the form of ionic liquid species. The main aim of this Review is to attract a broad audience of chemical, biological, and medical scientists to study advantages of ionic liquid pharmaceutics. Overall, the discussed data highlight the importance of the research direction defined as "Ioliomics", studies of ions in liquids in modern chemistry, biology, and medicine.

S. Paruthi, L. Brooks, C. D’Ambrosio et al.

Sleep is essential for optimal health in children and adolescents. Members of the American Academy of Sleep Medicine developed consensus recommendations for the amount of sleep needed to promote optimal health in children and adolescents using a modified RAND Appropriateness Method. The recommendations are summarized here. A manuscript detailing the conference proceedings and the evidence supporting these recommendations will be published in the Journal of Clinical Sleep Medicine. Paruthi S, Brooks LJ, D’Ambrosio C, Hall WA, Kotagal S, Lloyd RM, Malow BA, Maski K, Nichols C, Quan SF, Rosen CL, Troester MM, Wise MS. Recommended amount of sleep for pediatric populations: a consensus statement of the American Academy of Sleep Medicine. J Clin Sleep Med 2016;12(6):785–786.

Haidan Yuan, Q. Ma, L. Ye et al.

Natural products and traditional medicines are of great importance. Such forms of medicine as traditional Chinese medicine, Ayurveda, Kampo, traditional Korean medicine, and Unani have been practiced in some areas of the world and have blossomed into orderly-regulated systems of medicine. This study aims to review the literature on the relationship among natural products, traditional medicines, and modern medicine, and to explore the possible concepts and methodologies from natural products and traditional medicines to further develop drug discovery. The unique characteristics of theory, application, current role or status, and modern research of eight kinds of traditional medicine systems are summarized in this study. Although only a tiny fraction of the existing plant species have been scientifically researched for bioactivities since 1805, when the first pharmacologically-active compound morphine was isolated from opium, natural products and traditional medicines have already made fruitful contributions for modern medicine. When used to develop new drugs, natural products and traditional medicines have their incomparable advantages, such as abundant clinical experiences, and their unique diversity of chemical structures and biological activities.

H. Sies

“Oxidative stress” as a concept in redox biology and medicine has been formulated in 1985; at the beginning of 2015, approx. 138,000 PubMed entries show for this term. This concept has its merits and its pitfalls. Among the merits is the notion, elicited by the combined two terms of (i) aerobic metabolism as a steady-state redox balance and (ii) the associated potential strains in the balance as denoted by the term, stress, evoking biological stress responses. Current research on molecular redox switches governing oxidative stress responses is in full bloom. The fundamental importance of linking redox shifts to phosphorylation/dephosphorylation signaling is being more fully appreciated, thanks to major advances in methodology. Among the pitfalls is the fact that the underlying molecular details are to be worked out in each particular case, which is bvious for a global concept, but which is sometimes overlooked. This can lead to indiscriminate use of the term, oxidative stress, without clear relation to redox chemistry. The major role in antioxidant defense is fulfilled by antioxidant enzymes, not by small-molecule antioxidant compounds. The field of oxidative stress research embraces chemistry, biochemistry, cell biology, physiology and pathophysiology, all the way to medicine and health and disease research.

Amisha, Paras Malik, Monika Pathania et al.

Background: Artificial intelligence (AI) is the term used to describe the use of computers and technology to simulate intelligent behavior and critical thinking comparable to a human being. John McCarthy first described the term AI in 1956 as the science and engineering of making intelligent machines. Objective: This descriptive article gives a broad overview of AI in medicine, dealing with the terms and concepts as well as the current and future applications of AI. It aims to develop knowledge and familiarity of AI among primary care physicians. Materials and Methods: PubMed and Google searches were performed using the key words 'artificial intelligence'. Further references were obtained by cross-referencing the key articles. Results: Recent advances in AI technology and its current applications in the field of medicine have been discussed in detail. Conclusions: AI promises to change the practice of medicine in hitherto unknown ways, but many of its practical applications are still in their infancy and need to be explored and developed better. Medical professionals also need to understand and acclimatize themselves with these advances for better healthcare delivery to the masses.

V. Kaul, Sarah Enslin, S. Gross

Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep learning. Now AI systems are capable of analyzing complex algorithms and self-learning, we enter a new age in medicine where AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and improving workflow efficiency. This article presents a brief historical perspective on the evolution of AI over the last several decades and the introduction and development of AI in medicine in recent years. A brief summary of the major applications of AI in gastroenterology and endoscopy are also presented, which will be reviewed in further detail by several other articles in this issue of GIE.

H. D. Vet, C. Terwee, L. Mokkink et al.

K. Schmitz, K. Courneya, Charles Matthews et al.

P. Barnes, B. Bloom, R. Nahin

M. Sawka, L. M. Burke, E. Eichner et al.

T. Luo, Yuan Lu, Shi‐kai Yan et al.

G. Briganti, O. le Moine

Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.

K. Ericsson

L. Borrell, J. Elhawary, E. Fuentes-Afflick et al.

Race and Genetic Ancestry in Medicine U.S. health inequities won’t be eliminated by abandoning the use of race and ethnicity in research and clinical practice, since these variables capture key epi...

Maged N. Kamel Boulos, Peng Zhang

A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology’s history and main concepts. A number of application examples of digital twins for personalised medicine, public health, and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.

R. Allada, J. Bass

From the Department of Neurobiology, Northwestern University, Evanston (R.A.), and the Department of Medicine, Division of Endocrinology, Metabolism, and Molecular Medicine, Feinberg School of Medicine, Northwestern University, Chicago (J.B.) — both in Illinois. Address reprint requests to Dr. Bass at the Department of Medicine, Northwestern University, 303 E. Superior St., Lurie 7-107, Chicago, IL 60611, or at j-bass@ northwestern . edu.

C. Ardern, F. Büttner, R. Andrade et al.

Poor reporting of medical and healthcare systematic reviews is a problem from which the sports and exercise medicine, musculoskeletal rehabilitation, and sports science fields are not immune. Transparent, accurate and comprehensive systematic review reporting helps researchers replicate methods, readers understand what was done and why, and clinicians and policy-makers implement results in practice. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement and its accompanying Explanation and Elaboration document provide general reporting examples for systematic reviews of healthcare interventions. However, implementation guidance for sport and exercise medicine, musculoskeletal rehabilitation, and sports science does not exist. The Prisma in Exercise, Rehabilitation, Sport medicine and SporTs science (PERSiST) guidance attempts to address this problem. Nineteen content experts collaborated with three methods experts to identify examples of exemplary reporting in systematic reviews in sport and exercise medicine (including physical activity), musculoskeletal rehabilitation (including physiotherapy), and sports science, for each of the PRISMA 2020 Statement items. PERSiST aims to help: (1) systematic reviewers improve the transparency and reporting of systematic reviews and (2) journal editors and peer reviewers make informed decisions about systematic review reporting quality.

Deirdre K. Tobias, Jordi Merino, Abrar Ahmad et al.