Systems and methods for data normalization

A computing system for normalizing records, the computing system comprising:

• at least one memory;
• at least one processor;
• a component configured to train a plurality of machine learning models based on a first plurality of annotated records;
• a component configured to, for each of a second plurality of records, apply one or more of the plurality of machine learning models to the record, wherein applying each trained machine learning model to the record generates a code for the record and a corresponding confidence score;
• a component configured to determine whether a first confidence score for a first code exceeds a predetermined threshold;
• and
• a component configured to, after determining that the confidence score for the first code exceeds the predetermined threshold, append the first code to one or more records of the second plurality of records,
• wherein each of the component comprises computer-executable instructions stored in the at least one memory for execution by the computing system.

A computer-readable storage medium storing instructions that, when executed by a computing system having a memory and a processor, cause the computing system to perform a method for normalizing data, the method comprising:

• training a machine learning model based on a plurality of annotated records, each annotated record specifying at least one associated classification;
• receiving a new record;
• normalizing the new record at least in part by applying the trained machine learning model to the received new record, wherein applying the trained machine learning model to the new record generates a first classification for the new record and a confidence score for the first classification;
• determining that the confidence score for the first classification exceeds a predetermined threshold;
• and
• after determining that the confidence score for the first classification exceeds the predetermined threshold, appending the first record to include an indication of the first classification.

A method for normalizing medical records, the method comprising:

• receiving a plurality of annotated medical records, each annotated medical record having a number of fields and a corresponding annotation, the corresponding annotation representing a field from a common schema model;
• training a machine learning model based on the received plurality of annotated medical records, wherein the trained machine learning model is configured to identify at least one field from the common schema model for each of a plurality of fields associated with an input medical record;
• receiving a first medical record;
• normalizing the first medical record at least in part by applying the trained machine learning model to the received first medical record, wherein applying the trained machine learning model to the received first medical record identifies, for each of a plurality of fields associated with the first medical record, at least one field of the common schema model associated with the field associated with the first medical record;
• and
• appending each identified field of the common schema model to the first medical record.