Before the onset of COVID-19, the healthcare community was already moving to meet the challenges of a growing global population. By collecting record amounts of clinical data electronically and making significant progress on neural network-based AI approaches, the industry now has the potential to build powerful predictive analytics systems. The focus will accelerate the shift from a one-size-fits-all approach to individualized medicine. But several questions remain. What are the plausible outcomes for the world of predictive analytics in both the short and long term? What does the care pathway look like if everything is predicted? And with patient populations and healthcare needs increasing exponentially, how can the industry deliver care in a sustainable and cost-effective way? This comprehensive report, written by Jaquie Finn and Dr. Gavin Troughton with Cambridge Consultants, explores the possibilities. You’ll learn: How predictive analytics plays a part across all stages of the care pathway The foundational enablers for predictive analytics How healthcare economics figure into the equation Predictive analytics and today’s healthcare system The future of predictive analytics in healthcare

Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using Mplus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model. The statistical modeling program Mplus Version 8.2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results. Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM. Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of Mplus results using real data sets Introduces different methods for sample size estimate and statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using M plus.

Beginning Database Design, Second Edition provides short, easy-to-read explanations of how to get database design right the first time. This book offers numerous examples to help you avoid the many pitfalls that entrap new and not-so-new database designers. Through the help of use cases and class diagrams modeled in the UML, you'll learn to discover and represent the details and scope of any design problem you choose to attack. Database design is not an exact science. Many are surprised to find that problems with their databases are caused by poor design rather than by difficulties in using the database management software. Beginning Database Design, Second Edition helps you ask and answer important questions about your data so you can understand the problem you are trying to solve and create a pragmatic design capturing the essentials while leaving the door open for refinements and extension at a later stage. Solid database design principles and examples help demonstrate the consequences of simplifications and pragmatic decisions. The rationale is to try to keep a design simple, but allow room for development as situations change or resources permit. Provides solid design principles by which to avoid pitfalls and support changing needs Includes numerous examples of good and bad design decisions and their consequences Shows a modern method for documenting design using the Unified Modeling Language What you'll learn Avoid the most common pitfalls in database design. Create clear use cases from project requirements. Design a data model to support the use cases. Apply generalization and specialization appropriately. Secure future flexibility through a normalized design. Ensure integrity through relationships, keys, and constraints. Successfully implement your data model as a relational schema. Who this book is for Beginning Database Design, Second Edition is aimed at desktop power users, developers, database administrators, and others who are charged with caring for data and storing it in ways that preserve its meaning and integrity. Desktop users will appreciate the coverage of Excel as a plausible "database" for research systems and lab environments. Developers and database designers will find insight from the clear discussions of design approaches and their pitfalls and benefits. All readers will benefit from learning a modern notation for documenting designs that is based upon the widely used and accepted Universal Modeling Language.