Improving healthcare communication: AI-driven emotion classification in imbalanced patient text data with explainable models
Open accessAbstract
Sentiment analysis is crucial in healthcare to understand patients’ emotions, automatically identifying the feelings of patients suffering from serious illnesses (cancer, AIDS, or Ebola) with an artificial intelligence model that constitutes a major challenge to help health professionals. This study presents a comparative study on different machine learning (logistic regression, naive Bayes, and LightGBM) and deep learning models: long short-term memory (LSTM) and bidirectional encoder representations from transformers (BERT) for classify health feelings thanks to textual data related to patients with serious illnesses. Considering the class imbalance of the dataset, various resampling techniques are investigated. The approach is complemented by an explainable model, LIME, to understand the shortcomings of the classification results. The results highlight the superior performance of the BERT and LSTM models with an F1-score of 89%.
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Sentiment-aware conversational agents are designed to detect and respond to emotional distress.
“sentiment-aware conversational agents designed to detect and respond to emotional distress [49].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [49]
The Introduction describes AI models that detect patients’ feelings from text, and Additional information discusses identifying negative thoughts. Neither passage describes a conversational agent or says the system responds to emotional distress; the supported part is detection, not response. (LLM, conf=0.87)
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A., S., & R., S. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100230 · Cited by 432 · see citing works
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Sorted by citations
White, B. M., Aitken, G. L., Zink, J. A., Kheirkhah Rahimabad, P., Kumsa, F. A., Hashtarkhani, S., Rashid, R., Kheirinejad, S., Girdwood, T., Brett, C. L., et al. (2026) · JMIR Cancer · doi:10.2196/82336 · Cited by 2 · see citing works
“Others further illustrate these applications through immersive and affect-aware interventions, including AI-driven virtual reality (VR) therapy for anxiety and depression [47,48] and sentiment-aware conversational agents designed to detect and respond to emotional distress [49].”
- Authors
- Hamza-Cherif, S., Tani, L. F. K., & Settouti, N.
- Affiliations
- University of Abou Bekr Belkaïd (DZ)
- LabISEN (FR)
- Venue
- Advances in Technology Innovation
- Published
- 2024-04-30
- Volume
- 9
- Issue
- 2
- Pages
- 129-142
- ISSN
- 2415-0436 (ISSN-L)
- 2518-2994
- OpenAlex
- W4398148079
- Cited by
- 3
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Advances in Technology Innovation (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- emotion classification, healthcare communication, serious illness, class imbalance, resampling techniques, explainable AI, lime, BERT, LSTM, cancer patients
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