Book contents
- The Cambridge Handbook of Research Methods in Clinical Psychology
- The Cambridge Handbook of Research Methods in Clinical Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgments
- Part I Clinical Psychological Science
- Part II Observational Approaches
- Part III Experimental and Biological Approaches
- Part IV Developmental Psychopathology and Longitudinal Methods
- Part V Intervention Approaches
- Part VI Intensive Longitudinal Designs
- Part VII General Analytic Considerations
- 28 Reproducibility in Clinical Psychology
- 29 Meta-Analysis
- 30 Mediation, Moderation, and Conditional Process Analysis
- 31 Statistical Inference for Causal Effects in Clinical Psychology
- 32 Analyzing Nested Data
- 33 Missing Data Analyses
- 34 Machine Learning for Clinical Psychology and Clinical Neuroscience
- Index
- References
34 - Machine Learning for Clinical Psychology and Clinical Neuroscience
from Part VII - General Analytic Considerations
Published online by Cambridge University Press: 23 March 2020
- The Cambridge Handbook of Research Methods in Clinical Psychology
- The Cambridge Handbook of Research Methods in Clinical Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgments
- Part I Clinical Psychological Science
- Part II Observational Approaches
- Part III Experimental and Biological Approaches
- Part IV Developmental Psychopathology and Longitudinal Methods
- Part V Intervention Approaches
- Part VI Intensive Longitudinal Designs
- Part VII General Analytic Considerations
- 28 Reproducibility in Clinical Psychology
- 29 Meta-Analysis
- 30 Mediation, Moderation, and Conditional Process Analysis
- 31 Statistical Inference for Causal Effects in Clinical Psychology
- 32 Analyzing Nested Data
- 33 Missing Data Analyses
- 34 Machine Learning for Clinical Psychology and Clinical Neuroscience
- Index
- References
Summary
A rapid growth in computational power and an increasing availability of large, publicly accessible, multimodal data sets present new opportunities for psychology and neuroscience researchers to ask novel questions, and to approach old questions in novel ways. Studies of the personal characteristics, situation-specific factors, and sociocultural contexts that result in the onset, development, maintenance, and remission of psychopathology, are particularly well suited to benefit from machine learning methods. However, introductory textbooks for machine learning rarely tailor their guidance to the needs of psychology and neuroscience researchers. Similarly, the traditional statistical training of clinical scientists often does not incorporate these approaches. This chapter acts as an introduction to machine learning for researchers in the fields of clinical psychology and clinical neuroscience. It discusses these methods, illustrated through real and hypothetical applications in the fields of clinical psychology and clinical neuroscience. It touches on study design, selecting appropriate techniques, how (and how not) to interpret results, and more, to aid researchers who are interested in applying machine learning methods to clinical science data.
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- Publisher: Cambridge University PressPrint publication year: 2020
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