Titlebook: Mental Health Informatics; Margaret Lech,Insu Song,Joachim Diederich Book 2014 Springer-Verlag Berlin Heidelberg 2014 Computational Intell

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Detection and Prediction of Clinical Depression,ss. Depressed individuals suffer from varying degrees of psychomotor retardation or agitation. Emotionally, depression sufferers experience lasting feelings of hopelessness, anger, guilt, desperation and loneliness often leading to suicidal thoughts. The specific emotional state of a person sufferin

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Using Diagnostic Information to Develop a Machine Learning Application for the Effective Screening Disorders (ASD) diagnostic reports. The ability of the 2-Class SVM application to screen for ASD is compared with other screening instruments: Gillian Autism Rating Scale—Second Edition [.], Social Communication Questionnaire [.] and Social Responsiveness Scale [.]. It was also cross-validated and

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Mental Health Informatics: Current Approaches,works), and (4) mental health information management systems (e.g., electronic patient record). In particular, we review research on automated mental health assessment systems, highlighting the potential power of each in solving current mental health care problems.

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The Rise of Person-Centered Healthcare and the Influence of Health Informatics and Social Network Aracting with clients over social media and with how they present themselves online. Clinical and ethical issues associated with patient contact via social networks are are discussed and recommendations are presented.

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Book 2014onous communication between mental health care providers and clients as well as the automation of assessment and therapy. .Speech and language are particularly interesting from the viewpoint of psychological assessment. For instance, depression may change the characteristics of voice in individuals

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Using Diagnostic Information to Develop a Machine Learning Application for the Effective Screening refined based on a sample (. = 221). The classification performance of the SVM application was relatively better compared to the other instruments (accuracy = 83.7 %, precision = 98.8 %, sensitivity = 83.3 %, specificity = 88.9 %). A 1-Class SVM classification model was also described to highlight the usefulness of SVM with a skewed population.

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1860-949X ield.This book introduces approaches that have the potential to transform the daily practice of psychiatrists and psychologists. This includes the asynchronous communication between mental health care providers and clients as well as the automation of assessment and therapy. .Speech and language are