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DX Reveal

Uncover a variety of insights from extracted data ​

DX Reveal uses key value pairing to help your uncover insights that inform decisions across large volumes of data.


DX Reveal is a natural language processing (NLP) microservice that uses machine learning (ML) to uncover insights from extracted text. This service provides the ability to detect defined entities, classify content, detect and recognize over 100 language types, key-phrase extraction, content sentiment, and identify specific topics within a defined dataset.


  • Recognize Social Security Numbers, Invoice Numbers, IDs, People & Places

  • Determine positive or negative sentiment towards a topic

  • Identify & categorize topics

  • Extract targeted key phrases

  • Categorize and organize data by unstructured to structured- to help make decisions across large volumes of data

DX Software Medical Mock-up on Laptop

Use Case

Our medical version of the microservice, DX Reveal MD+, detects entities, medications, medical conditions, anatomy, medical tests, treatments, prescriptions, lab results, and procedures in the clinical text to standardized medical ontologies such as RxNorm, ICD-10-CM, and SNOMEDCT. The dashboard can interface with other medical systems, make guesses about diagnoses, flag patients who may need extra care, etc.

We are very proud of what DX Reveal can do. Recently, a new healthcare law was passed for Veterans allowing a presumptive list to be used to approve their medical requests automatically. Using a presumptive list that includes specific language and symptoms, DOMA can use DX Reveal- Summary Service to consolidate several medical documents for a veteran into a single summary that would tell an end-user whether the veteran could be automatically approved for a presumptive. We are excited that DX Reveal can expedite this process for Veterans.​

Contact Us

Contact us to schedule a personalized consultation and find out how we can make your organization work better for you with the DX Engage Software or Microservices.

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