Abstract: A system and method for extracting data from a received fax from a medical provider and matching it with a corresponding preauthorization record using modeling techniques. A received fax is converted into text via OCR, relevant keys are extracted from the text using modeling techniques and differential probabilities are calculated for each key that are then compared to the candidate preauthorization records using logistic regression models to find the most probable matching candidate records. Candidate record matches are ranked by matching probability and the highest ranked candidate record is considered the matching record to the received fax.

Abstract: A system trains a machine learning based model to predict the likelihood of an outcome for an entity, for example, a user. The system determines, for a particular prediction for a user, feature impact scores that indicate how each feature of the user impacted the prediction for that user. The feature impact scores are ranked to determine top driver features for the user that had the highest impact on the prediction. The system generates a human understandable description for the top driver features. The system provides the generated description for the top driver features for display, for example, via a user interface.

Abstract: A system trains a machine learning based model to predict the likelihood of an outcome for an entity, for example, a user. The system determines, for a particular prediction for a user, feature impact scores that indicate how each feature of the user impacted the prediction for that user. The feature impact scores are ranked to determine top driver features for the user that had the highest impact on the prediction. The system generates a human understandable description for the top driver features. The system provides the generated description for the top driver features for display, for example, via a user interface.