![]() If duplicates are identified, BUDDI.AI can highlight the various sections of documents where duplicates appear, and present a clinical named entity level a summary of all duplicates.īUDDI.AI can process longitudinal CCDs and/or unstructured physician notes to generate timeline representations of medicines prescribed to a given patient, procedures conducted, and/or symptoms identified or allergies across patient episodes, providers, and time.įinally, BUDDI.AI can auto-generate a summary of a given medical record for physicians to quickly glean the most critical clinical information about a patient without having to pore through tens or hundreds of pages of documents. Then, BUDDI.AI applies our Clinical Contextual algorithms to weave the relationships across elements within the medical record to form a “Clinical Contextual Graph,” which better represents the context of the entire patient episode.īUDDI.AI can further identify if any two documents are duplicates. Hence, BUDDI.AI applies post-OCR clean-up algorithms like predicting missing clinical keywords, fixing typos (powered by our proprietary clinical dictionary), and cleaning up the formatting to improve the veracity of the medical record.īUDDI.AI applies the industry’s best-in-class NLP + Knowledge Graph algorithms to first semi-structure the electronic document by tagging north of 1000+ clinical named entity objects. Most traditional OCR solutions do not yield optimal accuracy. The BUDDI.AI platform then re-stitches the medical records into a machine searchable electronic format.īUDDI.AI applies our proprietary OCR algorithms if needed to PDF-image documents and extracts the characters from the image based documents across multiple pages. ![]() BUDDI.AI then automatically parses out the datasets and maps the relationships of datasets between rows and columns. BUDDI.AI aggregates a patient’s longitudinal medical records in numerous formats such as:īUDDI.AI applies proprietary vision based algorithms to assess the presence of objects similar to tables or columns/rows or boxes in medical records or lab records. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |