Bio+Clinical BERT, BERT Base, and CNN Performance Comparison for
  Predicting Drug-Review Satisfaction
Yue Ling
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.