Abstract: The prediction system accesses a flowchart of questions relating to surgical cases and receives, for each of set of surgical case identifiers, surgical case information and an actual surgical case length. The prediction system trains a machine learning model to predict surgical case lengths using the surgical case information and prunes the flowchart by removing questions associated with a uniform set of answers. The prediction system receives, from a client device, a request to reserve an operating room for a surgical case, and transmits, for display via a user interface of the client device, questions from the flowchart. The prediction system receives a feature vector of answers to the transmitted questions from the client device and inputs a type surgical case and the feature vector to the machine learning model, which outputs a predicted surgical case length. The prediction system reserves an operating room for the predicted surgical case length.

Abstract: Method and system for cleansing and de-duplicating data in database are provided. The method includes filtering garbage records from a plurality of records based on data fields, and applying cleansing rules to create a cleansed database. A similarity vector is generated, where each vector corresponds to pairwise comparison of distinct data entries in cleansed database. Matching rules are applied to label each vector as one of matched, unmatched and unclassified. The method analyzes the vectors labeled as matched and unmatched to train a machine learning model to identify duplicates in the cleansed database. Unclassified vectors in the cleansed database are labeled as matched or unmatched by applying machine learning model on unclassified vectors. Thereafter, the method processes all the vectors labeled as matched to create clusters of records that are duplicates of each other. Further, records in each cluster are merged to obtain de-duplicated cleansed database using predefined consolidated rules.