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
- Input: Clinical notes or medical documentation in textual format.
- 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.
- 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
