Degeneration of nerve cells that control cognitive, speech, and language processes leading to linguistic impairments at various levels, from verbal utterances to individual speech sounds, could indicate signs of neurological, cognitive and psychiatric disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), dementias, depression, autism spectrum disorder, schizophrenia, etc. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. However, speech-based biomarkers could potentially offer many advantages over current clinical standards. In addition to being objective and naturalistic, they can also be collected remotely with minimal instruction and time requirements. Furthermore, Machine Learning algorithms developed to build automated diagnostic models using linguistic features extracted from speech could aid diagnosis of patients with probable diseases from a group of normal population. To ensure that speech-based biomarkers are providing accurate measurement and can serve as effective clinical tools for detecting and monitoring disease, speech features extracted and analyzed must be systematically and rigorously evaluated. Different machine learning architectures trained to classify different types of disordered speech must also be rigorously tested and systematically compared.

Drs. Leonard Scinto and Kirk Daffner provide a comprehensive survey of new diagnostic approaches to Alzheimer's disease. The authoritative contributors critically survey the most promising current research on early diagnostic markers for Alzheimer's disease, including the elucidation of changes in the brain revealed by structural and functional neuroimaging, as well as the characteristic patterns of cognitive decline that are documented by sensitive neuropsychological tests, various genetic markers, and biological assays. Early Diagnosis of Alzheimer's Disease illuminates the complex issues surrounding the search for early markers of this increasingly widespread disease. It will establish a new standard reference guide for all those working with Alzheimer's patients.

This up-to-date, superbly illustrated book is a practical guide to the effective use of neuroimaging in the patient with cognitive decline. It sets out the key clinical and imaging features of the various causes of dementia and directs the reader from clinical presentation to neuroimaging and on to an accurate diagnosis whenever possible. After an introductory chapter on the clinical background, the available "toolbox" of structural and functional neuroimaging techniques is reviewed in detail, including CT, MRI and advanced MR techniques, SPECT and PET, and image analysis methods. The imaging findings in normal ageing are then discussed, followed by a series of chapters that carefully present and analyze the key findings in patients with dementias. Throughout, a practical approach is adopted, geared specifically to the needs of clinicians (neurologists, radiologists, psychiatrists, geriatricians) working in the field of dementia, for whom this book will prove an invaluable resource.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

When Handbook of Normative Data for Neuropsychological Assessment was published in 1999, it was the first book to provide neuropsychologists with summaries and critiques of normative data for neuropsychological tests. The Second Edition, which has been revised and updated throughout, presents data for 26 commonly used neuropsychological tests, including: Trailmaking, Color Trails, Stroop Color Word Interference, Auditory Consonant Trigrams, Paced Auditory Serial Addition, Ruff 2 and 7, Digital Vigilance, Boston Naming, Verbal Fluency, Rey-Osterrieth Complex Figure, Hooper Visual Fluency, Design Fluency, Tactual Performance, Wechsler Memory Scale-Revised, Rey Auditory-Verbal learning, Hopkins Verbal learning, WHO/UCLA Auditory Verbal Learning, Benton Visual Retention, Finger Tapping, Grip Strength (Dynamometer), Grooved Pegboard, Category, and Wisconsin Card Sorting tests. In addition, California Verbal learning (CVLT and CVLT-II), CERAD ListLearning, and selective Reminding Tests, as well as the newest version of the Wechsler Memory Scale (WMS-III and WMS-IIIA), are reviewed. Locator tables throughout the book guide the reader to the sets of normative data that are best suited to each individual case, depending on the demographic characteristics of the patient, and highlight the advantages associated with using data for comparative purposes. Those using the book have the option of reading the authors' critical review of the normative data for a particular test, or simply turning to the appropriate data locator table for a quick reference to the relevant data tables in the Appendices. The Second Edition includes reviews of 15 new tests. The way the data are presented has been changed to make the book easier to use. Meta-analytic tables of predicted values for different ages (and education, where relevant) are included for nine tests that have a sufficient number of homogeneous datasets. No other reference offers such an effective framework for the critical evaluation of normative data for neuropsychological tests. Like the first edition, the new edition will be welcomed by practitioners, researchers, teachers, and graduate students as a unique and valuable contribution to the practice of neuropsychology.

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

The Boston Process Approach to neuropsychological assessment, advanced by Edith Kaplan, has a long and well-respected history in the field. However, its theoretical and empirical support has not previously been assembled in an easily accessible format. This volume fills that void by compiling the historical, empirical, and practical teachings of the Process Approach. The reader will find a detailed history of the precursors to this model of thought, its development through its proponents such as Harold Goodglass, Nelson Butters, Laird Cermak, and Norman Geschwind, and its continuing legacy. The second section provides a guide to applying the Boston Process Approach to some of the field's most commonly used measures, such as the various Wechsler Intelligence Scales, the Trail Making Test, the California Verbal Learning Test, and the Boston Naming Test. Here, the reader will find a detailed history of the empirical evidence for test administration and interpretation using Boston Process Approach tenets. The final section of the book provides various perspectives on the implementation of the Boston Process Approach in various clinical and research settings and with specialized populations.