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

Machine Learning (1971) Natural Language Processing (869) Methods and Feasibility Studies (420) Advanced Data Analytics in eHealth (196) Clinical Information and Decision Making (1807) Decision Support for Health Professionals (1468) Artificial Intelligence (1996) Innovations and Technology in Cancer Care (503)

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 (10) Tweetations (2) Metrics

Journals

  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
  3. Priego-Parra B, Remes-Troche J. Bidirectional relationship between gastrointestinal cancer and depression: The key is in the microbiota-gut-brain axis. World Journal of Gastroenterology 2024;30(48):5104 View
  4. Wang M, Fan S, Li Y, Gao B, Xie Z, Chen H. Robust multi-modal fusion architecture for medical data with knowledge distillation. Computer Methods and Programs in Biomedicine 2025;260:108568 View
  5. Ma M, Wang M, Wei L, Fei X, Chen H. Multi-modal fusion model for Time-Varying medical Data: Addressing Long-Term dependencies and memory challenges in sequence fusion. Journal of Biomedical Informatics 2025;165:104823 View
  6. Jin Y, Liu J, Li P, Wang B, Yan Y, Zhang H, Ni C, Wang J, Li Y, Bu Y, Wang Y. The Applications of Large Language Models in Mental Health: Scoping Review. Journal of Medical Internet Research 2025;27:e69284 View
  7. Bozcuk H, Güzel H, Arici M, Yildiz M, Koçer M, Kiliçtaş B, Artaç M, Karakaya G, Coşkun H. Artificial intelligence and patient narratives: A novel approach to assessing hope in patients with cancer. Medicine International 2025;5(4):1 View
  8. Shen Y, Wu P, Lin C, Yen C, Shan Y, Chen P. Machine Learning–Based Classification of Depression Using Inflammatory Biomarkers in Pancreatic Cancer Patients. The Kaohsiung Journal of Medical Sciences 2025 View
  9. Lin S, Groenwold R, Mehta H, Kim J, Segal J. Addressing Missingness in Predictive Models That Use Electronic Health Record Data. Annals of Internal Medicine 2025 View

Books/Policy Documents

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