Faculty Collaboration Database

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning. Sci Rep 2023 Dec 01;13(1):21200

Date

12/02/2023

Pubmed ID

38040784

Pubmed Central ID

PMC10692236

DOI

10.1038/s41598-023-48054-z

Scopus ID

2-s2.0-85178213950 (requires institutional sign-in at Scopus site)

Abstract

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

Author List

Tritt A, Yue JK, Ferguson AR, Torres Espin A, Nelson LD, Yuh EL, Markowitz AJ, Manley GT, Bouchard KE, TRACK-TBI Investigators

Authors

Michael McCrea PhD Professor in the Neurosurgery department at Medical College of Wisconsin
Lindsay D. Nelson PhD Associate Professor in the Neurosurgery department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Brain Injuries, Traumatic
Distillation
Humans
Machine Learning
Phenotype
Prognosis