Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials

Authors

Sayli M. Narkhede
Lauren Luther
Ian M. Raugh
Anna R. Knippenberg
Farnaz Zamani Esfahlani
Hiroki Sayama
Alex S. Cohen
Brian Kirkpatrick
Gregory P. Strauss

Document Type

Article

Publication Date

3-1-2022

Publication Source (Journal or Book title)

Schizophrenia Bulletin

First Page

425

Last Page

436

Recommended Citation

Narkhede, S. M., Luther, L., Raugh, I. M., Knippenberg, A. R., Esfahlani, F. Z., Sayama, H., Cohen, A. S., Kirkpatrick, B., & Strauss, G. P. (2022). Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials. Schizophrenia Bulletin, 48 (2), 425-436. https://doi.org/10.1093/schbul/sbab134