This repository demonstrates the use of BERT (Bidirectional Encoder Representations from Transformers) for medical diagnosis tasks, specifically predicting diseases based on symptom descriptions. The project integrates data augmentation techniques such as backtranslation and paraphrasing to enhance the model's performance and generalization.
The integration of BERT for diagnosis, combined with data augmentation techniques, improves the performance and robustness of the model. The following steps outline the approach used:
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Data Augmentation:
- Backtranslation: A text is translated into another language (e.g., from English to German) and then back to English. This generates alternative expressions of the same content while maintaining semantic meaning. It introduces linguistic variation, reducing overfitting and helping the model generalize better.
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BERT-Based Model:
- BERT is fine-tuned for medical diagnosis classification tasks. It is particularly effective for understanding complex medical terminology and nuances in symptoms.
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Workflow:
- Dataset Preparation: Symptom descriptions and disease labels are collected and preprocessed.
- Augmentation: Backtranslation and paraphrasing techniques are applied to augment the dataset.
- Fine-Tuning: The augmented dataset is used to fine-tune the BERT model for disease classification.
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Applications:
- This approach is valuable for Medical NLP tasks, enhancing diagnostic accuracy by leveraging diverse linguistic inputs. It can be used for predicting diseases based on symptom descriptions.