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

Formative Evaluation of Digital Health Interventions (2491) Patient-Reported Outcome Measures (PROMs) (294) Diabetes (194) Depression and Mood Disorders; Suicide Prevention (1392) Diagnostic Tools in Mental Health (248) Diabetes Health Services and System Innovations (119)

Published on in Vol 3, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13610, first published .
Use of Patient-Reported Data to Match Depression Screening Intervals With Depression Risk Profiles in Primary Care Patients With Diabetes: Development and Validation of Prediction Models for Major Depression

Use of Patient-Reported Data to Match Depression Screening Intervals With Depression Risk Profiles in Primary Care Patients With Diabetes: Development and Validation of Prediction Models for Major Depression

Use of Patient-Reported Data to Match Depression Screening Intervals With Depression Risk Profiles in Primary Care Patients With Diabetes: Development and Validation of Prediction Models for Major Depression

Authors of this article:

Haomiao Jin1, 2 Author Orcid Image ;   Shinyi Wu1, 2, 3 Author Orcid Image
Article Authors Cited by (6) Tweetations (1) Metrics

Journals

  1. Jin H, Wu S. Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study. Journal of Medical Internet Research 2020;22(3):e17282 View
  2. Jin H, Chien S, Meijer E, Khobragade P, Lee J. Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study. JMIR Mental Health 2021;8(5):e27113 View
  3. Morelli D, Dolezalova N, Ponzo S, Colombo M, Plans D. Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population. Frontiers in Psychiatry 2021;12 View
  4. Jin H, Nath S, Schneider S, Junghaenel D, Wu S, Kaplan C. An informatics approach to examine decision-making impairments in the daily life of individuals with depression. Journal of Biomedical Informatics 2021;122:103913 View
  5. Abdulazeem H, Whitelaw S, Schauberger G, Klug S, Vathy-Fogarassy Á. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLOS ONE 2023;18(9):e0274276 View

Books/Policy Documents

  1. Liu H, Zhang W, Goh C, Dai F, Sadiq S, Tse G. Internet of Things and Machine Learning for Type I and Type II Diabetes. View