Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

ABSTRACT:

Objective

Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury.

Methods

SuperLearner, an ensemble machine-learning algorithm, was applied to prospective observational cohort data from 1494 critically-injured patients. Over 1000 agnostic predictors were used to generate prediction models from multiple candidate learners for outcomes of interest at serial time points post-injury. Model accuracy was estimated using cross-validation and area under

SUBMITTER: Christie SA 

PROVIDER: S-EPMC6457612 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature