Academic Journal
Analysing Emotional Well-Being in Cancer Patients: A Natural Language Processing Approach to Correlating Text with Hospital Anxiety and Depression Scale Scores.
| Title: | Analysing Emotional Well-Being in Cancer Patients: A Natural Language Processing Approach to Correlating Text with Hospital Anxiety and Depression Scale Scores. |
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| Authors: | Alemdar MS; Department of Medical Oncology, Istinye University, Istanbul 34396, Turkey.; Department of Medical Oncology, Medical Park Hospital, Antalya 07160, Turkey., Bozcuk HŞ; Department of Medical Oncology, Lara Anatolia Hospital, Antalya 07230, Turkey. |
| Source: | Current oncology (Toronto, Ont.) [Curr Oncol] 2026 Jul 04; Vol. 33 (7). Date of Electronic Publication: 2026 Jul 04. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 9502503 Publication Model: Electronic Cited Medium: Internet ISSN: 1718-7729 (Electronic) Linking ISSN: 11980052 NLM ISO Abbreviation: Curr Oncol Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2021- : Basel, Switzerland : MDPI Original Publication: Toronto : Multimed, c1994- |
| MeSH Terms: | Depression*/psychology , Depression*/etiology , Depression*/diagnosis , Anxiety*/psychology , Anxiety*/etiology , Neoplasms*/psychology , Natural Language Processing*, Humans ; Female ; Male ; Middle Aged ; Cross-Sectional Studies ; Adult ; Aged ; Psychological Well-Being ; Emotions ; Surveys and Questionnaires ; Quality of Life |
| Abstract: | Background: Psychological distress, particularly anxiety and depression, is highly prevalent among cancer patients, and is associated with impaired quality of life, reduced treatment adherence, and increased mortality risk. Standardized screening instruments, such as the Hospital Anxiety and Depression Scale (HADS), are effective, but face implementation barriers in busy oncology outpatient settings. This cross-sectional study investigated whether BERT-based Natural Language Processing (NLP) analysis of brief patient-generated free texts would correlate with HADS scores in a consecutive cohort of cancer outpatients. Material and Methods: A total of 165 consecutive adult cancer outpatients were enrolled at a tertiary oncology center in Turkey. All participants completed the HADS questionnaire and were asked to write freely about their current emotional state in Turkish. Patient-generated texts were analyzed using a pre-trained Turkish BERT model to derive a continuous BERT Sentiment Score (BSS) and a categorical BERT Sentiment Cluster (BSC) via unsupervised hierarchical clustering. Univariate and multivariate linear regression analyses were performed to examine associations between clinical, demographic, and NLP-derived variables and the logarithmically transformed HADS score. Results: The mean total HADS score was 10.46 (range, 0-33), consistent with a moderate level of psychological distress. In multivariate analysis, two variables were independently associated with HADS scores: female sex (β = 0.20, t = 2.14, p = 0.034), associated with higher HADS scores, and BERT Sentiment Score (BSS) (β = -0.18, t = -2.43, p = 0.016), with higher values corresponding to lower HADS scores. Hierarchical clustering identified two distinct thematic groups: 'Coping and Fighting Spirit' (74%), and 'Hope and Negative Feelings' (26%); however, cluster membership (BSC) was not independently associated with HADS scores (β = -0.02, p = 0.789). Clinical variables, including cancer stage, diagnosis type, treatment status, and time since diagnosis, also were not independently associated with HADS scores. Conclusions: BERT-based sentiment analysis of brief patient-generated free texts yielded a continuous measure that independently correlated with HADS scores in cancer outpatients, alongside female sex. These findings provide proof-of-concept evidence that NLP-derived sentiment scoring may offer a practical, scalable, and complementary approach to standardized psychological screening in routine oncology care. |
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| Contributed Indexing: | Keywords: BERT; artificial intelligence; cancer; hospital anxiety and depression scale; natural language processing; oncology; psychological distress; sentiment analysis |
| Entry Date(s): | Date Created: 20260727 Date Completed: 20260727 Latest Revision: 20260813 |
| Update Code: | 20260814 |
| PubMed Central ID: | PMC13409533 |
| DOI: | 10.3390/curroncol33070400 |
| PMID: | 42505202 |
| Database: | MEDLINE |
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