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Data to Information: Computational Models and Analytic Methods

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Mental Health Informatics

Part of the book series: Health Informatics ((HI))

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Abstract

Computational models and analytic methods are increasingly important in the modeling and analyses of mental health and illness. The increased availability 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 approaches generate explanatory models that describe the mechanisms of neural or psychological processes. Data-driven approaches typically extract predictive relations between variables and relevant outcomes or uncover patterns such as disease subtypes in data. Machine learning methods are increasingly used to develop models, especially data-driven models. This chapter will describe key methods and application examples, the workflow in machine learning, data preprocessing, feature selection methods, and the main categories of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning, and deep learning. In addition, this chapter will briefly describe standards for reporting models and ethical and safety issues related to the development and use of computational models.

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Authors and Affiliations

  1. University of Pittsburgh, Pittsburgh, USA

    Shyam Visweswaran & Mohammadamin Tajgardoon

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  1. Shyam Visweswaran
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  2. Mohammadamin Tajgardoon
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Correspondence to Shyam Visweswaran .

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Editors and Affiliations

  1. Duke University, Durham, NC, USA

    Jessica D. Tenenbaum

  2. Six Aims for Behavioral Health, Minneapolis, MN, USA

    Piper A. Ranallo

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Visweswaran, S., Tajgardoon, M. (2021). Data to Information: Computational Models and Analytic Methods. In: Tenenbaum, J.D., Ranallo, P.A. (eds) Mental Health Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-70558-9_10

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  • DOI: https://doi.org/10.1007/978-3-030-70558-9_10

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