Bio+Clinical BERT, BERT Base, and CNN Performance Comparison for Predicting Drug-Review Satisfaction
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Abstract (arXiv)
The objective of this study is to develop natural language processing (NLP) models that can analyze patients' drug reviews and accurately classify their satisfaction levels as positive, neutral, or negative. Such models would reduce the workload of healthcare professionals and provide greater insight into patients' quality of life, which is a critical indicator of treatment effectiveness. To achieve this, we implemented and evaluated several classification models, including a BERT base model, Bio+Clinical BERT, and a simpler CNN. Results indicate that the medical domain-specific Bio+Clinical BERT model significantly outperformed the general domain base BERT model, achieving macro f1 and recall score improvement of 11%, as shown in Table 2. Future research could explore how to capitalize on the specific strengths of each model. Bio+Clinical BERT excels in overall performance, particularly with medical jargon, while the simpler CNN demonstrates the ability to identify crucial words and accurately classify sentiment in texts with conflicting sentiments.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 2 Aug 2023.
Builds on
5 of 13 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2019Publicly Available Clinical BERT Embeddingscited by 1,002
- 2020KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Mediacited by 84
- 2021Lexicon-based Methods vs. BERT for Text Sentiment Analysiscited by 4
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysiscited by 2
- 2025Integrated ensemble of BERT- and features-based models for authorship attribution in Japanese literary workscited by 1
- 2026Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generationcited by 0
- 2026Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation2 Jun 2026
- 2025SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis14 Nov 2025
- 2025Integrated ensemble of BERT- and features-based models for authorship attribution in Japanese literary works11 Apr 2025