Stabilizing Sparse Cox Model using Clinical Structures in Electronic Medical Records

TL;DR

This paper presents a method to improve the stability of sparse Cox models for clinical predictions by incorporating inherent EMR structures, enhancing feature stability without sacrificing predictive accuracy.

Contribution

The authors introduce a novel approach that leverages clinical structures in EMRs to stabilize sparse Cox models for time-to-event predictions.

Findings

Increased feature stability with clinical structures

Maintained predictive performance with AUC of 0.64

Significant improvement in stability measures

Abstract

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using clinical structures inherent in Electronic Medical Records. Model estimation is stabilized using a feature graph derived from two types of EMR structures: temporal structure of disease and intervention recurrences, and hierarchical structure of medical knowledge and practices. We demonstrate the efficacy of the method in predicting time-to-readmission of heart failure patients. On two stability measures - the Jaccard index and the Consistency index - the use of clinical structures significantly increased feature stability without hurting discriminative power. Our model reported a competitive AUC of 0.64 (95% CIs: [0.58,0.69]) for 6 months prediction.