This paper is in the following e-collection/theme issue:

Diagnostic Tools in Mental Health (200) Artificial Intelligence (922) New Methods (800) Machine Learning (1194) Digital Biomarkers and Digital Phenotyping (155) Psychotic Disorders (146)

Published on in Vol 7, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19348, first published .
Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

Authors of this article:

Michael Leo Birnbaum 1, 2, 3 Author Orcid Image ;   Prathamesh "Param" Kulkarni 4 Author Orcid Image ;   Anna Van Meter 1, 2, 3 Author Orcid Image ;   Victor Chen 5 Author Orcid Image ;   Asra F Rizvi 2, 3 Author Orcid Image ;   Elizabeth Arenare 2, 3 Author Orcid Image ;   Munmun De Choudhury 5 Author Orcid Image ;   John M Kane 1, 2, 3 Author Orcid Image
Article Authors Cited by (9) Tweetations (18) Metrics

Michael Leo Birnbaum   1, 2, 3 * , MD ;   Prathamesh "Param" Kulkarni   4 * , PhD ;   Anna Van Meter   1, 2, 3 , PhD ;   Victor Chen   5 , BSc ;   Asra F Rizvi   2, 3 , MA ;   Elizabeth Arenare   2, 3 , BA ;   Munmun De Choudhury   5 , PhD ;   John M Kane   1, 2, 3 , MD

1 Hofstra Northwell School of Medicine, Hempstead, NY, United States

2 The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States

3 The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States

4 Cornell Tech, Cornell University, New York City, NY, United States

5 Georgia Institute of Technology, Atlanta, GA, United States

*these authors contributed equally

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