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

Machine Learning (1465) Natural Language Processing (692) Methods and Feasibility Studies (380) Advanced Data Analytics in eHealth (133) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Artificial Intelligence (1371) Innovations and Technology in Cancer Care (423)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51925, first published .
Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study

Authors of this article:

Anne de Hond1, 2, 3 Author Orcid Image ;   Marieke van Buchem1, 2, 3 Author Orcid Image ;   Claudio Fanconi3, 4 Author Orcid Image ;   Mohana Roy5 Author Orcid Image ;   Douglas Blayney5 Author Orcid Image ;   Ilse Kant1, 6 Author Orcid Image ;   Ewout Steyerberg1, 2 Author Orcid Image ;   Tina Hernandez-Boussard3, 7, 8 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

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  1. Drobotun O, Kolotilov N, Konovalenko V, Konovalenko S, Ternovyy N. COMORBIDITY IN ONCOLOGY: MODERN CHALLENGES AND THE SEARCH FOR WAYS TO SOLVE THE PROBLEM. Clinical and Preventive Medicine 2024;(3):132 View
  2. van Buchem M, de Hond A, Fanconi C, Shah V, Schuessler M, Kant I, Steyerberg E, Hernandez-Boussard T. Applying natural language processing to patient messages to identify depression concerns in cancer patients. Journal of the American Medical Informatics Association 2024;31(10):2255 View

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  1. Irmak-Yazicioglu M, Arslan A. Recent Advances and Challenges in the Treatment of Major Depressive Disorder. View