Systems and methods for natural language processing documentation review system for clinical decision support

A non-transitory computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs comprising instructions for:

• receiving a clinical document and a policy type relating to the clinical document, the clinical document comprising at least one page;
• generating a processed page, by
• detecting whether the page includes an orientation skew;
• de-skewing the page;
• detecting whether the page includes noise;
• and
• removing noise from the page;
• generating extracted text from the processed page using optical character recognition (OCR);
• analyzing the extracted text to generate a classification label for the page using a multinomial classifier model comprising a long short-term memory (LSTM) neural network, wherein the multinomial classifier model has been trained to recognize different classes of page, based on similarities to a corpus of historical pages that have been previously classified;
• recognizing a specified keyword within the extracted text, using a named entity recognition model (NER), wherein the NER is trained using policy data relating to a policy having the policy type, and wherein the specified keyword is identified as relating to the policy type;
• and
• displaying a visual representation of the clinical document on a display, wherein the visual representation comprises a visual indicator of the presence of the specified keyword on the page and a visual indicator of the classification label.

A system for automating clinical documentation review, the system comprising:

• one or more processors;
• memory;
• and
• one or more programs stored in the memory, wherein the one or more programs are configured for execution by the one or more processors and include instructions for:
• receiving a clinical document and a policy type relating to the clinical document, the clinical document comprising at least one page;
• generating a processed page, by
• detecting whether the page includes an orientation skew;
• de-skewing the page;
• detecting whether the page includes noise;
• and
• removing noise from the page;
• generating extracted text from the processed page using optical character recognition (OCR);
• analyzing the extracted text to generate a classification label for the page using a multinomial classifier model comprising a long short-term memory (LSTM) neural network, wherein the multinomial classifier model has been trained to recognize different classes of page, based on similarities to a corpus of historical pages that have been previously classified;
• recognizing a specified keyword within the extracted text, using a named entity recognition model (NER), wherein the NER is trained using policy data relating to a policy having the policy type, and wherein the specified keyword is identified as relating to the policy type;
• and
• displaying a visual representation of the clinical document on a display, wherein the visual representation comprises a visual indicator of the presence of the specified keyword on the page, and a visual indicator of the classification label.