Title
Data science and predictive analytics : biomedical and health applications using R / Ivo D. Dinov.
Edition
Second edition.
ISBN
9783031174834 (electronic bk.)
3031174836 (electronic bk.)
9783031174827
3031174828
Published
Cham : Springer, 2022.
Language
English
Description
1 online resource (1 volume)
Other Standard Identifiers
10.1007/978-3-031-17483-4 doi
Call Number
QA76.9.B45
Dewey Decimal Classification
005.7
Summary
Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in this textbook address specific knowledge gaps, resolve educational barriers, and mitigate workforce information readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical foundations, modern computational methods, advanced data science techniques, model-based machine learning (ML), model-free artificial intelligence (AI), and innovative biomedical applications. The books fourteen chapters start with an introduction and progressively build the foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. Individual modules and complete end-to-end pipeline protocols are available as functional R electronic markdown notebooks. These workflows support an active learning platform for comprehensive data manipulation, sophisticated analytics, interactive visualization, and effective dissemination of open problems, current knowledge, scientific tools, and research findings. This Second Edition includes new material reflecting recent scientific and technological progress and a substantial content reorganization to streamline the covered topics. Featured are learning-based strategies utilizing generative adversarial networks (GANs), transfer learning, and synthetic data generation. There are complete end-to-end examples of ML/AI training, prediction, and assessment using quantitative, qualitative, text, and imaging datasets. This textbook is suitable for self-learning and instructor-guided course training. It is appropriate for upper division and graduate-level courses covering applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide spectrum of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory and funding agencies.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Series
Springer series in applied machine learning.
Chapter 1 - Introduction
Chapter 2: Basic Visualization and Exploratory Data Analytics
Chapter 3: Linear Algebra, Matrix Computing and Regression Modeling
Chapter 4: Linear and Nonlinear Dimensionality Reduction
Chapter 5: Supervised Classification
Chapter 6: Black Box Machine Learning Methods
Chapter 7: Qualitative Learning Methods - Text Mining, Natural Language Processing, Apriori Association Rules Learning
Chapter 8: Unsupervised Clustering
Chapter 9: Model Performance Assessment, Validation, and Improvement
Chapter 10: Specialized Machine Learning Topics
Chapter 11: Variable Importance and Feature Selection
Chapter 12: Big Longitudinal Data Analysis
Chapter 13: Function Optimization
Chapter 14: Deep Learning, Neural Networks.
