Healthcare Data Enrichment

Healthcare Data Enrichment is a key component of building high performing machine learning models. An organization's healthcare data alone can provide good predictive performance; however, enriching your in house dataset can truly create powerful and high-performing models. Enrichment allows a user to expand the number of possible features that can be created from a dataset. With Lumiata's enrichment functionality, we are able to extract over 5 million features from a normal health plan dataset.

Types of Enrichments

When raw data is ingested for a customer, each Person360 records is tagged or cross-coded by the following:

  • Lumiata Disease Codes
  • ICD-9 to SNOMED
  • ICD-9 to CCS
  • CPT to SNOMED
  • ICD-10 to SNOMED
  • ICD-10 to CCS
  • LOINC to SNOMED
  • NDC to RXNORM
  • NDC to SNOMED
  • NDC to ATC
  • HCC Risk Scores

Lumiata will also normalize lab data using Lumiata's Lab Interpreter functionality.


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