Finding a constrained number of predictor phenotypes for multiple outcome prediction.

ABSTRACT:

Background

Prognostic models help aid medical decision-making. Various prognostic models are available via websites such as MDCalc, but these models typically predict one outcome, for example, stroke risk. Each model requires individual predictors, for example, age, lab results and comorbidities. There is no clinical tool available to predict multiple outcomes from a list of common medical predictors.

Objective

Identify a constrained set of outcome-agnostic predictors.

Methods

We proposed a novel technique aggregating the standardised mean difference across hundreds of outcomes to learn a constrained set of predictors that appear to be predictive of many outcomes. Model performance was evaluated using the constrained set of predictors across eight prediction tasks. We

SUBMITTER: Reps JM 

PROVIDER: S-EPMC11751890 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature