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--- |
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license: mit |
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language: |
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- en |
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--- |
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# SkimLit: NLP Model for Medical Abstracts |
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SkimLit is a natural language processing (NLP) project aimed at making the reading of medical abstracts more accessible. This project replicates the methodology outlined in the paper "PubMed 200K RCT: a Dataset for Sequenctial Sentence Classification in Medical Abstracts," using TensorFlow and various deep learning techniques. |
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# Project Overview |
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# **`Section 1`** |
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## Data Collection |
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- The PubMed 200K RCT dataset is obtained from the author's GitHub repository using the following commands: |
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``` |
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git clone https://github.com/Franck-Dernoncourt/pubmed-rct |
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cd pubmed-rct/PubMed_20k_RCT_numbers_replaced_with_at_sign |
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``` |
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## Data Prepocessing |
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- Sentences are extracted from the dataset, and numeric labels are assigned for machine learning models. |
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- Three baseline models are established to set the foundation for more complex models. |
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## Baseline Model (Model 0) |
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- TF-IDF Multinomial Naive Bayes Classifier is implemented. |
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- Classification evaluation metrics such as accuracy, precision, recall, and F1-score are employed. |
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## Deep Sequence Models |
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### Model 1: Conv1D with Token Embeddings |
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- Custom TextVectorizer and text embedding layers are created. |
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- Data is optimized for efficiency using TensorFlow tf.data API. |
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### Model 2: Pretrained Token Embeddings |
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- Universal Sentence Encoder (USE) from TensorFlow Hub is used for feature extraction. |
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### Model 3: Conv1D with Character Embeddings |
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- Character-level tokenizer and embedding are implemented. |
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- Conv1D model is constructed using character embeddings. |
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### Model 4: Hybrid Embedding Layer |
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- Token and character-level embeddings are combined using layers.Concatenate. |
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- A model is developed to process both types of embeddings and output label probabilities. |
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### Model 5: Transfer Learning with Positional Embeddings |
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- Positional embeddings are introduced to enhance the model's understanding of the sequence. |
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- A tribrid embedding model is created, combining token, character, line_number, and total_lines features. |
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## Model Evaluation and Comparison |
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- Models are evaluated on various datasets to compare their performance. |
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## Save and Load Models |
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- Models are saved and loaded for future use. |
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## Model Loading and Evaluation |
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- Pre-trained models are loaded and evaluated on validation datasets. |
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## Test Dataset Processing and Prediction |
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- A test dataset is created, preprocessed, and used for making predictions with the loaded model. |
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## Enriching Test Dataframe with Predictions |
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- Predictions and additional columns are added to the test dataframe for analysis. |
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## Finding Top Wrong Predictions |
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- The top 100 most inaccurately predicted samples are identified. |
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## Investigating Top Wrong Predictions |
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- Detailed information on the top 10 wrong predictions is displayed. |
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# **`Section 2`** |
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## Example Abstracts |
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- Example abstracts are downloaded from a GitHub repository. |
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## Processing Example Abstracts with spaCy |
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- spaCy is used to parse sentences from example abstracts. |
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## One-Hot Encoding and Prediction on Example Abstracts |
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- Line numbers and total lines are one-hot encoded, and predictions are made using the loaded model. |
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## Visualizing Predictions on Example Abstracts |
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- Predicted sequence labels for each line in the abstract are displayed. |
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# Conclusion |
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- SkimLit provides a comprehensive exploration of NLP techniques for medical abstracts, from baseline models to sophisticated deep learning architectures. The models are evaluated, compared, and applied to real-world examples, offering insights into their strengths and limitations. |
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- Feel free to explore the code, experiment with different models, and contribute to the advancement of Skimlit NLP. |
