A Data-Driven Graph Model Approach to Critical Care Patient Clinical Trajectory Prediction

Abstract

The advent of increasingly available patient health data has created opportunities for critical care medicine to embrace modern data-driven decision support. However, existing methods, such as rule-based scoring systems or machine learning-based classification models, often focus on uni-dimensional patient outcomes such as mortality, lacking the granularity necessary to understand a patient's overall health during their hospital stay. This research focuses on predicting a patient's clinical trajectory, described by key health indicators readily accessible through electronic health records, by estimating correlations and causal relationships among these health indicators over time. To overcome the challenges associated with multi-dimensional, longitudinal, and heterogeneous patient data, we develop an analysis pipeline that first identifies distinct patient clusters with similar clinical trajectories through temporal pattern analysis. Then, for each cluster, we implement a structural equation model (SEM) to capture both immediate and time-lagged dependencies among variables, which offers a deeper understanding of the dynamic relationships. Directed acyclic graphs (DAGs) are used to visualize causal and correlative relationships, providing intuitive insights into how vital health indicators interact and form a patient's clinical trajectory. This integrated approach, which combines clustering, SEM, and DAG visualization, enables more refined modeling of patient clinical trajectories and has the potential to guide personalized interventions, ultimately improving patient outcomes in critical care settings.