| # Automated Medical Coding | |
| ## Overview | |
| Automated Medical Coding is an AI-driven model designed to streamline the process of extracting and assigning medical codes from clinical notes. This model leverages natural language processing (NLP) to predict **ICD (International Classification of Diseases)** and **CPT (Current Procedural Terminology)** codes based on unstructured text data, such as physician notes or medical documentation. | |
| Medical coding is a critical step in healthcare, facilitating accurate billing, claims processing, and statistical tracking. By automating this process, our model reduces manual effort, enhances accuracy, and saves time for healthcare providers. | |
| ## Features | |
| - Predicts **ICD codes**, which categorize diagnoses and medical conditions. | |
| - Predicts **CPT codes**, which detail medical services and procedures. | |
| - Designed to handle clinical notes with complex, unstructured language. | |
| ## Base Model | |
| This model builds upon the **[Microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract]**, a pretrained transformer model fine-tuned for medical text understanding. BiomedBERT's capability to process medical jargon makes it an ideal foundation for this task. | |
| ## How It Works | |
| 1. **Input:** Clinical notes or medical documentation in textual format. | |
| 2. **Processing:** The input text is tokenized and passed through BiomedBERT for feature extraction. Additional fully connected layers process these features to predict corresponding ICD and CPT codes. | |
| 3. **Output:** A list of ICD and CPT codes relevant to the input clinical notes. | |
| ## Benefits | |
| - **Improved Efficiency:** Reduces manual coding time for medical professionals. | |
| - **Increased Accuracy:** Minimizes errors in coding and improves billing accuracy. | |
| - **Scalability:** Can process large volumes of clinical notes effectively. | |
| ## Sample Model Prediction | |
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