Accepted for/Published in: JMIR Cancer
Date Submitted: Apr 26, 2022
Open Peer Review Period: Apr 25, 2022 - Jun 20, 2022
Date Accepted: Jun 20, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring the Association of Cancer and Depression in Electronic Health Records Combining Encoded Diagnosis and Mining Free Text Clinical Notes
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.
Objective:
The main objectives of this study were 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 two 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 different sources of clinical information related to depression. This included ICD-9-CM diagnosis codes and structured information extracted by mining free-text clinical notes via Natural Language Processing (NLP) tools based on SNOMED-CT, mentioning 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 two types of cancer considered. We managed to identify a higher number of patients with depression by mining free-text clinical notes when compared to the group selected exclusively on ICD-9-CM codes, increasing the number of patients diagnosed with depression by 34.7% (441/1269).
Conclusions:
This study provides new clinical evidence of the depression-cancer comorbidity and supporting the use of NLP for processing free-text clinical notes from electronic health records (EHR), contributing to the identification of additional clinical data that complements those provided by coded data improving the management of patients.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.