Linguistic Biomarkers Of Neurological Cognitive And Psychiatric Disorders Verification Analytical Validation Clinical Validation And Machine Learning
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Linguistic Biomarkers of Neurological, Cognitive, and Psychiatric Disorders: Verification, Analytical Validation, Clinical Validation, and Machine Learning
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.
Biomarkers in Psychiatry
This volume addresses one of the Holy Grails in Psychiatry, namely the evidence for and potential to adopt ‘Biomarkers’ for prevention, diagnosis, and treatment responses in mental health conditions. It meshes together state of the art research from international renowned pre-clinical and clinical scientists to illustrate how the fields of anxiety disorders, depression, psychotic disorders, and autism spectrum disorder have advanced in recent years.
Artificial Intelligence in Healthcare
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