Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

ABSTRACT: Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for

SUBMITTER: Chou A 

PROVIDER: S-EPMC8989303 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature