The project aims to create an AI model that assesses the risks of claim denials. The current system consists of two main components: the Autoencoder and the Denial Brain.

1. Autoencoder

Components

• PDF Parsing and Computer Vision Module: Converts PDF to structured text.
• Free Text Coder: Converts text information about procedure or diagnosis into CPT/HCPCS/ICD-10 codes.

Workflow

NLP Parsing: Extracts text from PDF files and identifies keywords using regular expressions.

Code Extraction and Validation: Uses pattern matching and the Free Text Coder to convert values into codes, and validates against a code dictionary.

RegEx Extractor: Tries to extract codes from text using regular expressions.

Code Validator: Verifies obtained codes against a dictionary.

TFIDF Extractor: Extracts codes based on text similarity, returning the nearest code in the vocabulary.

• Computer Vision: Converts PDF to image, fitting it into a model to yield text blocks. Locates keywords in boxes using letterwise matching and Levenstein distance.

• Merge Outputs: Combines NLP and Computer Vision outputs to create the final Autoencoder output.

2. Denial Brain

Data Preparation:

• LabelEncoder is used for categorical variables.
• The target set to the amount paid for the claim.

Data Processing

• The dataset contained above 7M samples, split into test and train sets (20%/80%).
• The most important features were the bill amount and procedure.

Model:

• Used a Random Forest as a regression algorithm to predict the paid amount.
• Hyperparameters: n_estimators=10, max_depth=None, max_features='auto', min_samples_split as needed.

Performance Evaluation: Used the mean squared error (MSE) score to estimate prediction performance.

Programming Languages

• Python 3
• SQL (specifically AWS Redshift dialect)

AI Stack:

• Prediction Modelling: Random Forest Extreme Gradient Boosting Machine Generalized Linear Models

• Computer Vision: Image Segmentation Text Detection

Optical Character Recognition (OCR)

• Natural Language Processing: Regular Expression TF-IDF Models

Containerization: Docker

Frameworks and Libraries:

Data Manipulation and Analysis:

• NumPy
• pandas Visualization:
• matplotlib
• seaborn

Machine Learning and AI:

• sci-kit-learn
• xgboost
• TensorFlow
• Keras

Database Connectivity:

• psycopg2
• pypyodbc

Web Frameworks:

• Flask
• Werkzeug Utilities:
• easydict
• tqdm
• Cython

Computer Vision and Image Processing:

• opencv-python
• pdf2image
• python-dfbox
• scipy