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

Diagnostic Tools in Mental Health (242) Artificial Intelligence (1371) New Methods (944) Machine Learning (1465) Digital Biomarkers and Digital Phenotyping (191) Psychotic Disorders (160)

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 Birnbaum1, 2, 3 Author Orcid Image ;   Prathamesh "Param" Kulkarni4 Author Orcid Image ;   Anna Van Meter1, 2, 3 Author Orcid Image ;   Victor Chen5 Author Orcid Image ;   Asra F Rizvi1, 2 Author Orcid Image ;   Elizabeth Arenare1, 2 Author Orcid Image ;   Munmun De Choudhury5 Author Orcid Image ;   John M Kane1, 2, 3 Author Orcid Image
Article Authors Cited by (10) Tweetations (17) Metrics

Journals

  1. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  2. Baumeister H, Bauereiss N, Zarski A, Braun L, Buntrock C, Hoherz C, Idrees A, Kraft R, Meyer P, Nguyen T, Pryss R, Reichert M, Sextl T, Steinhoff M, Stenzel L, Steubl L, Terhorst Y, Titzler I, Ebert D. Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders. Frontiers in Psychiatry 2021;12 View
  3. Enriquez J, Chu Y, Pudakalakatti S, Hsieh K, Salmon D, Dutta P, Millward N, Lurie E, Millward S, McAllister F, Maitra A, Sen S, Killary A, Zhang J, Jiang X, Bhattacharya P, Shams S. Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer. JMIR Medical Informatics 2021;9(6):e26601 View
  4. Ferrara M, Franchini G, Funaro M, Cutroni M, Valier B, Toffanin T, Palagini L, Zerbinati L, Folesani F, Murri M, Caruso R, Grassi L. Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment. Current Psychiatry Reports 2022;24(12):925 View
  5. Nguyen V, Lu N, Kane J, Birnbaum M, De Choudhury M. Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study. JMIR Mental Health 2022;9(12):e39747 View
  6. Mulinari S. Short-circuiting biology: Digital phenotypes, digital biomarkers, and shifting gazes in psychiatry. Big Data & Society 2023;10(1) View
  7. Chen Z, Kulkarni P, Galatzer-Levy I, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3(11):100602 View
  8. He Y, Matsunaga M, Li Y, Kishi T, Tanihara S, Iwata N, Tabuchi T, Ota A. Classifying Schizophrenia Cases by Artificial Neural Network Using Japanese Web-Based Survey Data: Case-Control Study. JMIR Formative Research 2023;7:e50193 View
  9. Gleeson J, McGuckian T, Fernandez D, Fraser M, Pepe A, Taskis R, Alvarez-Jimenez M, Farhall J, Gumley A. Systematic review of early warning signs of relapse and behavioural antecedents of symptom worsening in people living with schizophrenia spectrum disorders. Clinical Psychology Review 2024;107:102357 View
  10. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View