Abstract: Embodiments are disclosed for automatically evaluating records. In the context of a method, an example embodiment includes receiving a set of text produced from a record, identifying, by block manipulation circuitry and from the set of text, one or more blocks of text that are related to a potential conclusion regarding the set of text, and extracting, by the block manipulation circuitry, the one or more blocks of text. The example method further includes concatenating, by the block manipulation circuitry, the extracted one or more blocks into a sequence of words, inputting the sequence of words into a machine learning model, and, in response to inputting the sequence of words into the machine learning model, producing, using the machine learning model, an indication of whether the potential conclusion regarding the record is supported by the sequence of words. Corresponding apparatuses and computer program products are also provided.

Abstract: A method, apparatus and computer program product group records based upon a prediction of the content of the records. In the context of a method, data associated with respective subjects of the records is received and a threshold of a machine learning model is adjusted to satisfy an accuracy requirement for record categorization. In response to analyzing the data, but not the records, by the machine learning model, the method separates, using the machine learning model, the records into the first and second groups with the first group including records that the associated data indicates are more likely to support the addition of a code and the second group including records that the associated data indicates are less likely to support the addition of a code. The method also includes subsequently processing the records in different manners depending upon whether the records are included in the first or second group.

Abstract: A method, apparatus and computer program product refine an automated coding model, such as for a medical chart. For each respective candidate code from a set of candidate codes, the method predicts a probability of the respective code being contained in a medical chart. The method also selects one of the candidate codes as being contained in the medical chart based upon the probability and removes the selected candidate code from the set of candidate codes. The method then repeatedly predicts the probability of a respective code being contained in the medical chart, selects one of the candidate codes based upon the predicted probability and removes the selected candidate code from the set of candidate codes. The method further determines a categorical crossentropy loss as to permit adjustment of one or more parameters of the automated coding model.

Abstract: A method, apparatus and computer program product are provided for classifying pages of a document with a linear regression model, and a deep learning (non-linear) model utilizing a neural network. The classification of each page is determined by determining which of the linearly predicted category or the non-linearly predicted category to use, such as for transmission to an auditor. Pages of medical records generated by concatenating reports from distinct sources are classified according to both models, and embodiments determine which classification should be used. The classifications may be optionally smoothed for continuity. The classifications may be sent to auditors and used to review and audit the medical records.