An Explainable and Statistically Validated Ensemble Clustering Model Applied to the Identification of Traumatic Brain Injury Subgroups.
Massive amounts of data are being collected and analyzed using various learning models with the objective of deriving useful discoveries that could transform or advance our society. Learning from the data collected is playing an increasingly important role in improving the quality of our healthcare. Machine learning (ML) can obtain insights into potential cause and effect for diseases and other conditions related to healthcare. This talk presents a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of analysis to enhance the quality and the explainability of results. This methodology is applicable to other clinical data sets that exhibit significant heterogeneity as well as other diverse data science applications in biomedicine and elsewhere.
Zoom Meeting ID: 969 7429 9371