For a target disease with its name as input, the ONCE system can generate a list of related EHR features:

• Narrative features:
  • Standardized clinical concepts represented by concept unique identifiers (CUIs) according to the unified medical language system (UMLS)
• Codified features:
  • PheCodes mapped from ICD codes;
  • Ingredient-level RxNorm codes mapped from Medication prescriptions;
  • Grouped CPT procedure codes according to the Clinical Classifications Software;
  • LOINC codes mapped from laboratory tests.

The output of ONCE can be used as input features for predictive modeling of a target disease phenotype (see a paired phenotyping app - KOMAP ;

• Representative learning: The key component that powers the ONCE feature selection engine is the semantic representations of all EHR codified and NLP concepts, trained via multi-source knowledge graph representation learning.
• Online article knowledge: To identify an initial set of clinical concepts important to a target disease, the ONCE system additionally leverages a large knowledge base of medical articles that describe the relatedness between narrative features and the disease at a higher level. The current article corpus comprises data from seven online sources including Wikipedia, Medscape, and Merck Manuals.
• A composite feature score: The final ONCE selection criteria are based on a composite score that integrates information on:
  • Relatedness of a candidate concept to the target concept as quantified by the semantic embeddings;
  • Frequency of the candidate concept in the EHR;
  • Weighted frequency of the concept in the disease-related knowledge source articles.

• Step 1: Type in your disease of interest;
• Step 2: Choose the main CUI(s) and the main PheCode corresponding to the disease. ONCE already select several candidates for you. You can select multiple main CUIs and a single main PheCode among them.
• Step 3: Just click the "Search" button and you are ready to go!