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

Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1514) Machine Learning (1465) Depression and Mood Disorders; Suicide Prevention (1363) Diagnostic Tools in Mental Health (242)

Published on in Vol 21, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13809, first published .
Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach

Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach

Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach

Authors of this article:

Suranga N Kasthurirathne1, 2 Author Orcid Image ;   Paul G Biondich1, 3 Author Orcid Image ;   Shaun J Grannis1, 3 Author Orcid Image ;   Saptarshi Purkayastha4 Author Orcid Image ;   Joshua R Vest1, 2 Author Orcid Image ;   Josette F Jones4 Author Orcid Image
Article Authors Cited by (19) Tweetations (26) Metrics

Journals

  1. Bickman L. Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. Administration and Policy in Mental Health and Mental Health Services Research 2020;47(5):795 View
  2. Dervaux A. L’intelligence artificielle suffira-t-elle à sortir la psychiatrie de la crise. French Journal of Psychiatry 2019;1:S71 View
  3. Decker B, Hill C, Baldassano S, Khankhanian P. Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches. Seizure 2021;85:138 View
  4. Rodríguez-Ruiz J, Galván-Tejada C, Vázquez-Reyes S, Galván-Tejada J, Gamboa-Rosales H. Classification of Depressive Episodes Using Nighttime Data; a Multivariate and Univariate Analysis. Programming and Computer Software 2020;46(8):689 View
  5. Anmella G, Primé-Tous M, Segú X, Solanes A, Ruíz V, Martín-Villalba I, Morilla I, Also-Fontanet A, Sant E, Murgui S, Sans-Corrales M, Murru A, Zahn R, Young A, Vicens V, Viñas-Bardolet C, Martínez-Cerdá J, Blanch J, Radua J, Fullana M, Cavero M, Vieta E, Hidalgo-Mazzei D. PRimary carE digital Support ToOl in mental health (PRESTO): Design, development and study protocols. Spanish Journal of Psychiatry and Mental Health 2024;17(2):114 View
  6. Lee K, Ham B. Machine Learning on Early Diagnosis of Depression. Psychiatry Investigation 2022;19(8):597 View
  7. Matthews E, Savoy M, Paranjape A, Washington D, Hackney T, Galis D, Zisman-Ilani Y. Acceptability of Health Information Exchange and Patient Portal Use in Depression Care Among Underrepresented Patients. Journal of General Internal Medicine 2022;37(15):3947 View
  8. Sun Y, Kong Z, Song Y, Liu J, Wang X. The validity and reliability of the PHQ-9 on screening of depression in neurology: a cross sectional study. BMC Psychiatry 2022;22(1) View
  9. Bhadra S, Kumar C. An insight into diagnosis of depression using machine learning techniques: a systematic review. Current Medical Research and Opinion 2022;38(5):749 View
  10. Borna S, Maniaci M, Haider C, Maita K, Torres-Guzman R, Avila F, Lunde J, Coffey J, Demaerschalk B, Forte A. Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications. Healthcare 2023;11(18):2584 View
  11. Nickson D, Meyer C, Walasek L, Toro C. Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review. BMC Medical Informatics and Decision Making 2023;23(1) View
  12. Nickson D, Singmann H, Meyer C, Toro C, Walasek L. Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records. Diagnostic and Prognostic Research 2023;7(1) View
  13. Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatrics 2023;23(1) View
  14. Epizitone A, Moyane S, Agbehadji I. A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications. Journal of Multidisciplinary Healthcare 2023;Volume 16:4015 View
  15. Heyat M, Akhtar F, Munir F, Sultana A, Muaad A, Gul I, Sawan M, Asghar W, Iqbal S, Baig A, de la Torre Díez I, Wu K. Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function. Complex & Intelligent Systems 2024;10(4):5883 View

Books/Policy Documents

  1. Kasthurirathne S, Grannis S. Clinical Informatics Study Guide. View
  2. Sailaja V, Yelamarthi M, Nandyala A, Manda M, Yamini K, Balusu V. Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. View
  3. Dixon B, Holmgren A, Adler-Milstein J, Grannis S. Clinical Informatics Study Guide. View