Abstract: A method for predicting errors in prescription claim data is performed by a claim analysis device. The method includes extracting historical claim features from successfully processed historical claims received from the data warehouse system. The method includes extracting pending claim features from a pending claim. The method includes applying a binarization process on the extracted historical claim features to obtain a binarized training feature set. The method includes applying the binarization process on the extracted pending claim features to obtain a binarized pending feature set. The method includes calculating an aggregate distance between the binarized pending feature set and the binarized training feature set. The method includes identifying the historical claim associated with the least aggregate distance as a predictive historical claim.

Abstract: A method includes receiving at least one data object and identifying, for a first aspect of the at least one data object, text strings of free form textual information having a first text string type. The method also includes generating updated free form textual information by removing the at least one text string having the first text string type. The method also includes generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram. The method also includes using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information.

Abstract: A method includes receiving at least one data object and identifying, for a first aspect of the at least one data object, text strings of free form textual information having a first text string type. The method also includes generating updated free form textual information by removing the at least one text string having the first text string type. The method also includes generating one or more feature vectors based on the updated free form textual information using at least one of a unigram, a bigram, and a trigram. The method also includes using an artificial intelligence engine that uses at least one machine learning model configured to provide, using the one or more feature vectors, an output that includes at least one prediction indicating at least one resource domain and a weight value indicating a probability that the at least one resource domain corresponds to the free form textual information.