Titlebook: Mental Health Informatics; Enabling a Learning Jessica D. Tenenbaum,Piper A. Ranallo Textbook 2021 Springer Nature Switzerland AG 2021 Men

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Data to Information: Computational Models and Analytic Methods,lity of data, the development of a wide range of analytic methods, and powerful and ubiquitous computing capability provide an unprecedented opportunity to develop computational models. Broadly speaking, two types of computational models are used in the context of mental health. Theory-based approac

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BUOY

Integrative Paradigms for Knowledge Discovery in Mental Health: Overcoming the Fragmentation of Knoty has brought considerable challenges in terms of how to facilitate efficient knowledge discovery and integration across disciplines in the domain. The vocabulary and semantic frameworks in use across these different descriptive levels are fragmented and contested, and it is difficult to gain an ov

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Natural Language Processing in Mental Health Research and Practice,post, a diary, or a transcribed conversation between two or more people. Although the quantity and richness of this unstructured narrative text is vast, it is inaccessible to traditional computer systems which rely on structured data. Natural language processing (NLP) is a technology that solves the

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Information Visualization in Mental Health Research and Practice,conomic statuses, and environmental exposures is an ongoing struggle for researchers, administrators, clinicians, public health experts, and patients who struggle to use data to understand mental health. Information visualization techniques combining rich displays of data with highly responsive user

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Informatics Technologies in the Diagnosis and Treatment of Mental Health Conditions,lassically, mental disorders are diagnosed, and treatment plans determined, based on extended interviews to collect patient reported symptoms and histories, careful evaluation by well-trained clinicians, and an often Odyssean journey to reach a satisfactory treatment plan. Mental health informatics

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Ethical, Legal, and Social Issues (ELSI) in Mental Health Informatics,four groups: (1) ethical issues related to artificial intelligence in mental healthcare, (2) issues related to mobile health and eHealth applications, (3) sociotechnical issues related to data sharing, advocacy, and genomics in mental health informatics, and (4) relevant laws and regulations includi

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