JMIR Publications

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.