Linguistic Biomarkers of Neurological, Cognitive, and Psychiatric Disorders: Verification, Analytical Validation, Clinical Validation, and Machine Learning

Linguistic Biomarkers of Neurological, Cognitive, and Psychiatric Disorders: Verification, Analytical Validation, Clinical Validation, and Machine Learning
Author :
Publisher : Frontiers Media SA
Total Pages : 122
Release :
ISBN-10 : 9782832552971
ISBN-13 : 2832552978
Rating : 4/5 (71 Downloads)

Book Synopsis Linguistic Biomarkers of Neurological, Cognitive, and Psychiatric Disorders: Verification, Analytical Validation, Clinical Validation, and Machine Learning by : Ratree Wayland

Download or read book Linguistic Biomarkers of Neurological, Cognitive, and Psychiatric Disorders: Verification, Analytical Validation, Clinical Validation, and Machine Learning written by Ratree Wayland and published by Frontiers Media SA. This book was released on 2024-08-07 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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