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

Formative Evaluation of Digital Health Interventions (2823) Patient-Reported Outcome Measures (PROMs) (325) Diabetes (226) Depression and Mood Disorders; Suicide Prevention (1520) Diagnostic Tools in Mental Health (280) Diabetes Health Services and System Innovations (130)

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 (7) 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
  2. Bourkhime H, Qarmiche N, Omari M, Charef N, Elghazi S, Tachfouti N, Fakir S, Berraho M, Otmani N. Intersection of Artificial Intelligence, Data Science, and Cutting-Edge Technologies: From Concepts to Applications in Smart Environment. View