Sebanyak 149 item atau buku ditemukan

Dinamika etika dan hukum kedokteran dalam tantangan zaman

Medical ethics and analysis on Indonesian medical law.

Hak dan Kewajiban dalam Profesi Kedokteran Pendahuluan Dalam
pelaksanaan profesi kedokteran sering kali dijumpai konflik antara dokter
dengan pasien, yang tidak dapat dipecahkan oleh kaidah-kaidah etika. Dalam
keadaan seperti ini maka kaidah hukum dapat diberlakukan, sehingga
pembicaraan tidak akan dapat dilepaskan dari masalah hak dan kewajiban dari
pihak-pihak yang terlibat dalam perselisihan atau perkara tersebut. Hal ini
disebabkan karena pada akhirnya ...

Anesthesiology: Today and Tomorrow

Annual Utah Postgraduate Course in Anesthesiology 1985

Certainly it is difficult to sufficiently recognize all those people responsible for
making this as successful a course as it is today. Each successive course
chairman — myself, Dr. Bergman, Dr. Loehning, Dr. Elwyn, Dr. Stanley, Dr. Petty
— has made a contribution to its success. Mr. Ed Lauder, who helped with many
of the physical and fiscal arrangements, the many faithful secretaries and staff
assistants (including Mrs. Rachel Melville, Mrs. Mollie Sato, and Mrs. Vicki Larsen
) the exhibitors ...

DATA MINING OF POST GENOME-WID

This dissertation, "Data Mining of Post Genome-wide Association Studies and Next Generation Sequencing" by 桂宏胜, Hongsheng, Gui, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Genome-wide association studies (GWAS) have been successfully applied to several complex diseases, yielding many confirmed associations. Nonetheless, at most they have explained half the genetic variance and often much less. It is quite apparent the rich GWAS datasets contain far more information than is typically uncovered using the most common univariate analysis approaches. The focus of the present thesis is on methods to extract the most information from GWAS and on post-GWAS experimental strategies, divided in four broad approaches. The first approach involves use of candidate gene studies to explore epistasis and gene by environment interactions, using samples of two different disorders, Hirschsprung disease (HSCR) and cognitive decline. For HSCR, previous studies identified rare and common variants in two genes, RET and NRG1, to be predisposing to disease, and further demonstrated a statistical interaction between common variants in these two genes. In this thesis, joint effects between common and rare variants both within and across the two genes were demonstrated by statistical modelling and then supported by functional interaction. For cognitive decline, SNPs previously implicated in Alzheimer's disease were examined for epistasis and gene-environment interaction in an independent sample of elderly Chinese. The ACE rs1800764_C heterozygote in combination with below-college educational level was found to result in greater cognitive decline. These two studies demonstrate the utility of post-GWAS candidate gene studies in detectinginteraction effects. The next two approaches were adopted on GWAS summary statistics at the SNP level. One of them involves meta-analysis applied to 11 epilepsy GWAS datasets, to increase power and explore whether findings are population-specific or general across populations. Two novel susceptibility genes (SCN1a and PCDH7) were identified using this approach. Furthermore, the previously identified epilepsy risk variant CAMSAP1L1 was found to only be a risk factor for Chinese focal epilepsy patients. The other summary statistic approach involved the development of a revised GWAS pathway analysis pipeline to search for effective genes or gene-sets. Its application to two autoimmune diseases revealed that multiple pathways might be dysfunctional simultaneously and hence contribute jointly to disease status. In addition, it indicated the pipeline was powerful for mining moderate/small genetic effects on common disorders. The last approach to post-GWAS analysis involves the use of next-generation sequencing (NGS). To this end, an automated NGS pipeline for variant calling, filtering and prioritization was established, specifically designed for gene burden analysis, recurrent gene sharing and de novo mutation (DNM) identification. The pipeline was applied to NGS sequencing of 62 candidate genes and also whole exomes of HSCR patients and their parents. Results indicated that multiple rare damaging inherited variants in several genes contribute to HSCR; in addition, loss of function DNMs were significantly enriched in HSCR probands. This thesis demonstrates the utility of data mining approaches for the dissection and exploration of genetic determinants of complex diseases. Such methods and their results should ultimately contribute to genetic diagnosis and improving treatment for complex disorders. Subjects: Data

This dissertation, "Data Mining of Post Genome-wide Association Studies and Next Generation Sequencing" by 桂宏胜, Hongsheng, Gui, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative ...

Data Mining to Determine Risk in Medical Decisions

Decisions regarding the risks involved in medical treatments must belong to patients and their physicians – after all, it is the patient's health and life which is at stake. But patients will not be equipped for this decision-making process if they cannot be given some idea as to the risks and benefits of treatment. Such risks are generally estimated by a consensus panel of specialist physicians using supporting medical literature. Unfortunately, this literature does not always provide a good estimate of risk, particularly in the case of rare occurrences. This book demonstrates statistical techniques that can be used to investigate matters of risk. These include kernel density estimation, predictive modeling, association rules and text analysis. It also shows, through example, how these techniques can provide meaningful results, and examines current methods, discussing some of the flaws in models which may lead to misleading results. After a general introduction to the concept of medical risk, the subjects covered include the process by which rare occurrences are investigated in drugs or treatments, the trade-offs between risks and benefits, extrapolation of clinical trial results and the cost of healthcare in relation to risks. It also examines problems such as competing risks, error, and the use of group identities, as well as looking at the issue of futility. The book concludes with a chapter providing a general discussion and summary, and an appendix shows some of the processes for using SAS Enterprise Miner to perform some of the models used in the text.

Unfortunately, this literature does not always provide a good estimate of risk, particularly in the case of rare occurrences. This book demonstrates statistical techniques that can be used to investigate matters of risk.

Data Mining, Systems Analysis, and Optimization in Biomedicine

This book is a collection of a sample of the latest research methods in data mining across diverse fields of biomedicine, neuroscience, engineering, and computer science. The problems and methods discussed in this book will be of great interest to new and established theoreticians and practitioners in these fields and provide them with new directions for research. The book will be required reading for biomedical and industrial engineers, computer scientists, and medical doctors.

This book is a collection of a sample of the latest research methods in data mining across diverse fields of biomedicine, neuroscience, engineering, and computer science.

Data Mining in Biomedicine

This volume presents an extensive collection of contributions covering aspects of the exciting and important research field of data mining techniques in biomedicine. Coverage includes new approaches for the analysis of biomedical data; applications of data mining techniques to real-life problems in medical practice; comprehensive reviews of recent trends in the field. The book addresses incorporation of data mining in fundamental areas of biomedical research: genomics, proteomics, protein characterization, and neuroscience.

Mining. in. EEG: Application. to. Epileptic. Brain. Disorders*. P.M. Pardalos2, L.D.
Iasemidis3 , W. Suharitdamrong2, D.-S. Shiau2, L.K. Dance2, O.A. Prokopyev4,
V.L. Boginski5, PR. Carney2, and J.C. Sackellares2 1 Rutgers University, USA ...

Data Mining in Grid Computing Environments

Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students interested in this field.

Rahul Ramachandran, Sara Graves, John Rushing, Ken Keizer, Manil Maskey,
Hong Lin and Helen Conover ABSTRACT The Algorithm Development and
Mining System (ADaM) was originally developed in the early 1990s with the goal
of ...

Pharmaceutical Data Mining

Approaches and Applications for Drug Discovery

Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.

This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug ...