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

Innovations and Technology in Cancer Care (466) Clinical Information and Decision Making (1662) Depression and Mood Disorders; Suicide Prevention (1523) Emotional, Social, Psychological Support for Cancer (216) Electronic Health Records (1187) Breast Cancer (247)

Published on in Vol 8, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39003, first published .
Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes

Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes

Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes

Authors of this article:

Angela Leis1, 2 Author Orcid Image ;   David Casadevall3, 4 Author Orcid Image ;   Joan Albanell3, 4 Author Orcid Image ;   Margarita Posso5, 6 Author Orcid Image ;   Francesc Macià5, 6 Author Orcid Image ;   Xavier Castells5, 6 Author Orcid Image ;   Juan Manuel Ramírez-Anguita1, 2 Author Orcid Image ;   Jordi Martínez Roldán7 Author Orcid Image ;   Laura I Furlong1, 2 Author Orcid Image ;   Ferran Sanz1, 2 Author Orcid Image ;   Francesco Ronzano2 Author Orcid Image ;   Miguel A Mayer1, 2 Author Orcid Image
Article Authors Cited by (2) Tweetations (15) Metrics

Journals

  1. Sim J, Huang X, Horan M, Baker J, Huang I. Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(4):467 View
  2. Yang Y, Han W, Zhang X, Yuan H, Wang R, Yang J, An C, Huang D. Depression-related innate immune genes and pan-cancer gene analysis and validation. Frontiers in Genetics 2025;15 View