Abstract: An Artificial Intelligence (AI) based document processing system receives a request including one or more of a message and documents related to a process to be automatically executed. A process identifier is extracted and used for retrieving guidelines for the automatic execution of the document processing task. Machine Learning (ML) models, each corresponding to a guideline, are used to extract data responsive to the guidelines. Based on the responsive data meeting the approval threshold and the automatic document processing task executed, one or more of a recommendation to accept or reject the request, and a corresponding letter can be automatically generated.

Abstract: A handwritten text processing system processes a digitized document including handwritten text input to generate an output version of the digitized document that allows users to execute text processing functions on the textual content of the digitized document. Each word of the digitized data is extracted by converting the digitized document into images, binarizing the images, and segmenting the images into binary image patches. Each binary image patch is further processed to identify if the word is machine-generated or if the word is handwritten. The output version is generated by combining underlying images of the pages of the digitized document with words from the pages superimposed in a transparent font at positions that coincide with the positions of the words in the underlying images.

Abstract: A device may receive, based on a request, document data identifying structured and unstructured documents associated with vaccinations received by users and may perform natural language processing on the document data to generate processed document data. The device may process the processed document data, with a machine learning model, to extract vaccination data from the processed document data and may transcribe the vaccination data into corresponding fields of a data structure. The device may receive, from a user device, a request for vaccination data associated with a user and may retrieve the vaccination data from the corresponding fields of the data structure based on the request. The device may provide the vaccination data, to the user device, to enable verification of the vaccination data.

Abstract: A handwritten text processing system processes a digitized document including handwritten text input to generate an output version of the digitized document that allows users to execute text processing functions on the textual content of the digitized document. Each word of the digitized data is extracted by converting the digitized document into images, binarizing the images, and segmenting the images into binary image patches. Each binary image patch is further processed to identify if the word is machine-generated or if the word is handwritten. The output version is generated by combining underlying images of the pages of the digitized document with words from the pages superimposed in a transparent font at positions that coincide with the positions of the words in the underlying images.

Abstract: An Artificial Intelligence (AI) based document processing system receives a request including one or more documents related to a process to be automatically executed. The information including the fields and an intent required for the process are extracted from one or more of the request and the documents. The required documents and fields are selected based on the intent and a domain model. The required fields are validated using external knowledge and the discrepancies identified therein are resolved. An internal master document is built based on the required fields. The internal master document is employed for the automatic execution of the process which can include a de-identification process or an appeal process.

Abstract: An Artificial Intelligence (AI) based document processing system receives a request including one or more of a message and documents related to a process to be automatically executed. A process identifier is extracted and used for retrieving guidelines for the automatic execution of the document processing task. Machine Learning (ML) models, each corresponding to a guideline, are used to extract data responsive to the guidelines. Based on the responsive data meeting the approval threshold and the automatic document processing task executed, one or more of a recommendation to accept or reject the request, and a corresponding letter can be automatically generated.

Abstract: An Artificial Intelligence (AI) based document processing system receives a request including one or more documents related to a process to be automatically executed. The information including the fields and an intent required for the process are extracted from one or more of the request and the documents. The required documents and fields are selected based on the intent and a domain model. The required fields are validated using external knowledge and the discrepancies identified therein are resolved. An internal master document is built based on the required fields. The internal master document is employed for the automatic execution of the process.

Abstract: A document processing system receives an electronic document including component documents generated from various sources in different formats. Plain text data can be extracted from the electronic document in addition to formatting and structuring information. The plain text data is segmented into sections and various entities are extracted and linked from the sections. An interactive graphical user interface (GUI) that displays content including the plain text data is formatted according to the styling information and annotated entity relationships are determined from the linked entities. The GUI enables user edits to the annotated entity relationships.

Abstract: An Artificial Intelligence (AI) based document processing system receives a request including one or more documents related to a process to be automatically executed. The information including the fields and an intent required for the process are extracted from one or more of the request and the documents. The required documents and fields are selected based on the intent and a domain model. The required fields are validated using external knowledge and the discrepancies identified therein are resolved. An internal master document is built based on the required fields. The internal master document is employed for the automatic execution of the process which can include a de-identification process or an appeal process.

Abstract: An Artificial Intelligence (AI) based document processing system receives a request including one or more documents related to a process to be automatically executed. The information including the fields and an intent required for the process are extracted from one or more of the request and the documents. The required documents and fields are selected based on the intent and a domain model. The required fields are validated using external knowledge and the discrepancies identified therein are resolved. An internal master document is built based on the required fields. The internal master document is employed for the automatic execution of the process.

Abstract: A document processing system receives an electronic document including component documents generated from various sources in different formats. Plain text data can be extracted from the electronic document in addition to formatting and structuring information. The plain text data is segmented into sections and various entities are extracted and linked from the sections. An interactive graphical user interface (GUI) that displays content including the plain text data is formatted according to the styling information and annotated entity relationships are determined from the linked entities. The GUI enables user edits to the annotated entity relationships.

Abstract: This document describes systems, methods, devices, and other techniques for automatically identifying and extracting medical conditions and supporting evidences from electronic health records. In some implementations, formatted text extracted from an unstructured electronic health record is obtained. The formatted text is segmented into multiple documents, wherein each document comprises a respective document type and represents a respective document encounter. Medical condition entities and supporting evidence entities referenced in each of the multiple documents are extracted. Extracted supporting evidence entities within a same document are linked to respective extracted medical condition entities from the same document using one or more of i) medical ontologies, or ii) a medical knowledge base. Output data representing linked supporting evidence entities and medical condition entities within a same document is provided.