Exploring the association of cancer and depression in electronic health records: combining encoded diagnosis and mining free-text clinical notes

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• dc.contributor.author Leis Machín, Angela 1974-
• dc.contributor.author Casadevall Aguilar, David
• dc.contributor.author Albanell Mestres, Joan
• dc.contributor.author Posso, Margarita
• dc.contributor.author Macià Guilà, Francesc Assís
• dc.contributor.author Castells, Xavier
• dc.contributor.author Ramírez Anguita, Juan Manuel
• dc.contributor.author Martínez Roldán, Jordi
• dc.contributor.author Furlong, Laura I., 1971-
• dc.contributor.author Sanz, Ferran
• dc.contributor.author Ronzano, Francesco
• dc.contributor.author Mayer, Miguel Ángel, 1960-
• dc.date.accessioned 2022-09-08T06:04:37Z
• dc.date.available 2022-09-08T06:04:37Z
• dc.date.issued 2022
• dc.description.abstract Background: A cancer diagnosis is a source of psychological and emotional stress, which are often maintained for sustained periods of time that may lead to depressive disorders. Depression is one of the most common psychological conditions in patients with cancer. According to the Global Cancer Observatory, breast and colorectal cancers are the most prevalent cancers in both sexes and across all age groups in Spain. Objective: This study aimed to compare the prevalence of depression in patients before and after the diagnosis of breast or colorectal cancer, as well as to assess the usefulness of the analysis of free-text clinical notes in 2 languages (Spanish or Catalan) for detecting depression in combination with encoded diagnoses. Methods: We carried out an analysis of the electronic health records from a general hospital by considering the different sources of clinical information related to depression in patients with breast and colorectal cancer. This analysis included ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) diagnosis codes and unstructured information extracted by mining free-text clinical notes via natural language processing tools based on Systematized Nomenclature of Medicine Clinical Terms that mentions symptoms and drugs used for the treatment of depression. Results: We observed that the percentage of patients diagnosed with depressive disorders significantly increased after cancer diagnosis in the 2 types of cancer considered-breast and colorectal cancers. We managed to identify a higher number of patients with depression by mining free-text clinical notes than the group selected exclusively on ICD-9-CM codes, increasing the number of patients diagnosed with depression by 34.8% (441/1269). In addition, the number of patients with depression who received chemotherapy was higher than those who did not receive this treatment, with significant differences (P<.001). Conclusions: This study provides new clinical evidence of the depression-cancer comorbidity and supports the use of natural language processing for extracting and analyzing free-text clinical notes from electronic health records, contributing to the identification of additional clinical data that complements those provided by coded data to improve the management of these patients.
• dc.description.sponsorship This research was carried out under the framework of the project Creating medically-driven integrative bioinformatics applications focused on oncology, CNS disorders and their comorbidities (MedBioinformatics, H2020-EU; grant 634143); and partially funded by the Institute of Health Carlos III (project IMPaCT-Data; IMP/00019) and cofunded by the European Union, European Regional Development Fund (“A way to make Europe”); and the Clinical Knowledge Aggregation by Mining Medical Reports (CliKA-MinE; PI17/00230), which is funded by Institute of Health Carlos III and cofunded by the European Union.
• dc.format.mimetype application/pdf
• dc.identifier.citation Leis A, Casadevall D, Albanell J, Posso M, Macià F, Castells X, Ramírez-Anguita JM, Martínez Roldán J, Furlong LI, Sanz F, Ronzano F, Mayer MA. Exploring the association of cancer and depression in electronic health records: combining encoded diagnosis and mining free-text clinical notes. JMIR Cancer. 2022 Jul 11;8(3):e39003. DOI: 10.2196/39003
• dc.identifier.doi http://dx.doi.org/10.2196/39003
• dc.identifier.issn 2369-1999
• dc.identifier.uri http://hdl.handle.net/10230/54018
• dc.language.iso eng
• dc.publisher JMIR Publications
• dc.relation.ispartof JMIR Cancer. 2022 Jul 11;8(3):e39003
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/634143
• dc.rights © Angela Leis, David Casadevall, Joan Albanell, Margarita Posso, Francesc Macià, Xavier Castells, Juan Manuel Ramírez-Anguita, Jordi Martínez Roldán, Laura I Furlong, Ferran Sanz, Francesco Ronzano, Miguel A Mayer. Originally published in JMIR Cancer (https://cancer.jmir.org), 11.07.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license information must be included.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Cancer
• dc.subject.keyword Depression
• dc.subject.keyword Electronic health records
• dc.subject.keyword Natural language processing
• dc.subject.keyword Text mining
• dc.title Exploring the association of cancer and depression in electronic health records: combining encoded diagnosis and mining free-text clinical notes
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion