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README.md

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-license: mit
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+---
+license: mit
+# 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](https://huggingface.co/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.
+
+## Example
+