Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes
BackgroundA 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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-07-01
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| Series: | JMIR Cancer |
| Online Access: | https://cancer.jmir.org/2022/3/e39003 |
| _version_ | 1797734930796511232 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Angela Leis |
| collection | DOAJ |
| description |
BackgroundA 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.
ObjectiveThis 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.
MethodsWe 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.
ResultsWe 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).
ConclusionsThis 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. |
| first_indexed | 2024-03-12T12:51:17Z |
| format | Article |
| id | doaj.art-76151f45329842ef9315cad5f9fd5de2 |
| institution | Directory Open Access Journal |
| issn | 2369-1999 |
| language | English |
| last_indexed | 2024-03-12T12:51:17Z |
| publishDate | 2022-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Cancer |
| spelling | doaj.art-76151f45329842ef9315cad5f9fd5de22023-08-28T22:43:06ZengJMIR PublicationsJMIR Cancer2369-19992022-07-0183e3900310.2196/39003Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical NotesAngela Leishttps://orcid.org/0000-0003-4780-7111David Casadevallhttps://orcid.org/0000-0002-3986-2084Joan Albanellhttps://orcid.org/0000-0003-1239-4580Margarita Possohttps://orcid.org/0000-0002-5053-257XFrancesc Maciàhttps://orcid.org/0000-0002-6090-5894Xavier Castellshttps://orcid.org/0000-0002-2528-0382Juan Manuel Ramírez-Anguitahttps://orcid.org/0000-0002-8509-0927Jordi Martínez Roldánhttps://orcid.org/0000-0002-4565-6228Laura I Furlonghttps://orcid.org/0000-0002-9383-528XFerran Sanzhttps://orcid.org/0000-0002-7534-7661Francesco Ronzanohttps://orcid.org/0000-0001-5037-9061Miguel A Mayerhttps://orcid.org/0000-0003-0362-6298 BackgroundA 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. ObjectiveThis 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. MethodsWe 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. ResultsWe 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). ConclusionsThis 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.https://cancer.jmir.org/2022/3/e39003 |
| spellingShingle | 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 Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes JMIR Cancer |
| title | Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes |
| title_full | Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes |
| title_fullStr | Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes |
| title_full_unstemmed | Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes |
| title_short | Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes |
| title_sort | exploring the association of cancer and depression in electronic health records combining encoded diagnosis and mining free text clinical notes |
| url | https://cancer.jmir.org/2022/3/e39003 |
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