Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery

ABSTRACT: Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised techniques are employed to cluster patients based on longitudinal recovery profiles. Subsequently, these data-driven clusters were assessed on clinical validity by experts and used as targets in supervised machine learning models. We present a formalised analysis of the obtained clusters that incorporates evaluation of (i) statistical and machine learning metrics, (ii) clusters clinical validity with descriptive statistics and medical expertise. Clusters quality assessment revealed that clusters obtained through a Bayesian method (High Dimensional

SUBMITTER: Stoitsas K 

PROVIDER: S-EPMC9550811 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature