In this review we explore recent developments in computerized adaptive diagnostic screening and computerized adaptive testing for the presence and severity of mental health disorders such as depression, anxiety, and mania. The statistical methodology is unique in that it is based on multidimensional item response theory (severity) and random forests (diagnosis) instead of traditional mental health measurement based on classical test theory (a simple total score) or unidimensional item response theory. We show that the information contained in large item banks consisting of hundreds of symptom items can be efficiently calibrated using multidimensional item response theory, and the information contained in these large item banks can be precisely extracted using adaptive administration of a small set of items for each individual. In terms of diagnosis, computerized adaptive diagnostic screening can accurately track an hour-long face-to-face clinician diagnostic interview for major depressive disorder (as an example) in less than a minute using an average of four questions with unprecedented high sensitivity and specificity. Directions for future research and applications are discussed.
|Original language||English (US)|
|Number of pages||22|
|Journal||Annual Review of Clinical Psychology|
|State||Published - Mar 28 2016|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS: This work was supported by grant R01-MH66302 from the National Institute of Mental Health.
Copyright ©2016 by Annual Reviews. All rights reserved.
- Computerized adaptive testing
- Differential item functioning
- Item response theory
- Mental health diagnosis
- Mental health measurement