Data Modeling for likelihood/risk/prediction

[Bassa1]

Data source = EHR datasets(need a way to demo these features from EHR)

Data type = real-world evidence(has connotation of FDA use)

• Note: CHP has used real-world evidence predicate, but is not from EHR

Predicate = ??

Biolink relation = related_to

• associated_with_increased_likelihood_of_concurrent --- associated_with_increased_likelihood_of_future
• associated_with_decreased_likelihood_of_concurrent --- associated_with_decreased_likelihood_of_future

Methods: Supervised machine learning, logistic regression (or other methods in future)

• Contemporaneous (same time): Can be used to mean the likelihood of a drug being given to patients with the disease X (not necessarily that drug treats disease X)
• Predict 2 months out: associated with likelihood a disease will occur, or a drug will be prescribed
• Predict 2 years out: associated with likelihood a disease will occur, or a drug will be prescribed

Decisions for Clinical Data Team

• Predicate
• Relation

Decisions for Multiomics EHR Risk KP

• Cut-offs --- No cut-offs for coefficient (have ARA) --- No cut-offs for p-value > 0.1 (have ARA)
• Coefficient log odds, NOT +/-
• Two predicate NOT one (increased/decreased)

[karafecho]

Closing this ticket, as predicates are now in place to support likelihood/risk: biolink:associated_with_increased_likelihood_of and biolink:associated_with_decreased_likelihood_of ...