دسترسی نامحدود
برای کاربرانی که ثبت نام کرده اند
برای ارتباط با ما می توانید از طریق شماره موبایل زیر از طریق تماس و پیامک با ما در ارتباط باشید
در صورت عدم پاسخ گویی از طریق پیامک با پشتیبان در ارتباط باشید
برای کاربرانی که ثبت نام کرده اند
درصورت عدم همخوانی توضیحات با کتاب
از ساعت 7 صبح تا 10 شب
ویرایش: 1 نویسندگان: Ton J. Cleophas, Aeilko H. Zwinderman (auth.) سری: SpringerBriefs in Statistics ISBN (شابک) : 9783319121628, 9783319121635 ناشر: Springer International Publishing سال نشر: 2014 تعداد صفحات: 132 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 9 مگابایت
کلمات کلیدی مربوط به کتاب یادگیری ماشین در پزشکی - کتاب آشپزی سه: زیست پزشکی عمومی، برنامه های کاربردی کامپیوتر، پزشکی/بهداشت عمومی، عمومی، ریاضیات محاسبات، آمار و محاسبات/برنامه های آمار
در صورت تبدیل فایل کتاب Machine Learning in Medicine - Cookbook Three به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب یادگیری ماشین در پزشکی - کتاب آشپزی سه نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Unique features of the book involve the following.
1.This book is the third volume of a three volume series of cookbooks entitled "Machine Learning in Medicine - Cookbooks One, Two, and Three". No other self-assessment works for the medical and health care community covering the field of machine learning have been published to date.
2. Each chapter of the book can be studied without the need to consult other chapters, and can, for the readership's convenience, be downloaded from the internet. Self-assessment examples are available at extras.springer.com.
3. An adequate command of machine learning methodologies is a requirement for physicians and other health workers, particularly now, because the amount of medical computer data files currently doubles every 20 months, and, because, soon, it will be impossible for them to take proper data-based health decisions without the help of machine learning.
4. Given the importance of knowledge of machine learning in the medical and health care community, and the current lack of knowledge of it, the readership will consist of any physician and health worker.
5. The book was written in a simple language in order to enhance readability not only for the advanced but also for the novices.
6. The book is multipurpose, it is an introduction for ignorant, a primer for the inexperienced, and a self-assessment handbook for the advanced.
7. The book, was, particularly, written for jaded physicians and any other health care professionals lacking time to read the entire series of three textbooks.
8. Like the other two cookbooks it contains technical descriptions and self-assessment examples of 20 important computer methodologies for medical data analysis, and it, largely, skips the theoretical and mathematical background.
9. Information of theoretical and mathematical background of the methods described are displayed in a "notes" section at the end of each chapter.
10.Unlike traditional statistical methods, the machine learning methodologies are able to analyze big data including thousands of cases and hundreds of variables.
11. The medical and health care community is little aware of the multidimensional nature of current medical data files, and experimental clinical studies are not helpful to that aim either, because these studies, usually, assume that subgroup characteristics are unimportant, as long as the study is randomized. This is, of course, untrue, because any subgroup characteristic may be vital to an individual at risk.
12. To date, except for a three volume introductary series on the subject entitled "Machine Learning in Medicine Part One, Two, and Thee, 2013, Springer Heidelberg Germany" from the same authors, and the current cookbook series, no books on machine learning in medicine have been published.
13. Another unique feature of the cookbooks is that it was jointly written by two authors from different disciplines, one being a clinician/clinical pharmacologist, one being a mathematician/biostatistician.
14. The authors have also jointly been teaching at universities and institutions throughout Europe and the USA for the past 20 years.
15. The authors have managed to cover the field of medical data analysis in a nonmathematical way for the benefit of medical and health workers.
16. The authors already successfully published many statistics textbooks and self-assessment books, e.g., the 67 chapter textbook entitled "Statistics Applied to Clinical Studies 5th Edition, 2012, Springer Heidelberg Germany" with downloads of 62,826 copies.
17. The current cookbook makes use, in addition to SPSS statistical software, of various free calculators from the internet, as well as the Konstanz Information Miner (Knime), a widely approved free machine learning package, and the free Weka Data Mining package from New Zealand.
18. The above software packages with hundreds of nodes, the basic processing units including virtually all of the statistical and data mining methods, can be used not only for data analyses, but also for appropriate data storage.
19. The current cookbook shows, particularly, for those with little affinity to value tables, that data mining in the form of a visualization process is very well feasible, and often more revealing than traditional statistics.
20.The Knime and Weka data miners uses widely available excel data files.
21. In current clinical research prospective cohort studies are increasingly replacing the costly controlled clinical trials, and modern machine learning methodologies like probit and tobit regressions as well as neural networks, Bayesian networks, and support vector machines prove to better fit their analysis than traditional statistical methods do.
22. The current cookbook not only includes concise descriptions of standard machine learning methods, but also of more recent methods like the linear machine learning models using ordinal and loglinear regression.
23. Machine learning tends to increasingly use evolutionary operation methodologies. Also this subject has been covered.
24. All of the methods described have been applied in the authors' own research prior to this publication.
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
Data Mining for Visualization of Health Processes (150 Patients with Pneumonia)....Pages 3-14
Training Decision Trees for a More Meaningful Accuracy (150 Patients with Pneumonia)....Pages 15-20
Front Matter....Pages 21-21
Variance Components for Assessing the Magnitude of Random Effects (40 Patients with Paroxysmal Tachycardias)....Pages 23-27
Ordinal Scaling for Clinical Scores with Inconsistent Intervals (900 Patients with Different Levels of Health)....Pages 29-33
Loglinear Models for Assessing Incident Rates with Varying Incident Risks (12 Populations with Different Drink Consumption Patterns)....Pages 35-38
Loglinear Modeling for Outcome Categories (Quality of Life Scores in 445 Patients with Different Personal Characteristics)....Pages 39-44
Heterogeneity in Clinical Research: Mechanisms Responsible....Pages 45-48
Performance Evaluation of Novel Diagnostic Tests (650 and 588 Patients with Peripheral Vascular Disease)....Pages 49-55
Front Matter....Pages 57-57
Spectral Plots for High Sensitivity Assessment of Periodicity (6 Years’ Monthly C Reactive Protein Levels)....Pages 59-64
Runs Test for Identifying Best Regression Models (21 Estimates of Quantity and Quality of Patient Care)....Pages 65-69
Evolutionary Operations for Process Improvement (8 Operation Room Air Condition Settings)....Pages 71-74
Bayesian Networks for Cause Effect Modeling (600 Patients Assessed for Longevity Factors)....Pages 75-80
Support Vector Machines for Imperfect Nonlinear Data (200 Patients with Sepsis)....Pages 81-85
Multiple Response Sets for Visualizing Clinical Data Trends (811 Patient Visits to General Practitioners)....Pages 87-93
Protein and DNA Sequence Mining....Pages 95-100
Iteration Methods for Crossvalidations (150 Patients with Pneumonia)....Pages 101-105
Testing Parallel-Groups with Different Sample Sizes and Variances (5 Parallel-Group Studies)....Pages 107-109
Association Rules Between Exposure and Outcome (50 and 60 Patients with Coronary Risk Factors)....Pages 111-115
Confidence Intervals for Proportions and Differences in Proportions....Pages 117-120
Ratio Statistics for Efficacy Analysis of New Drugs (50 Patients Treated for Hypercholesterolemia)....Pages 121-125
Back Matter....Pages 127-131