Leveraging Temporal Learning with Dynamic Range (TLDR) for Enhanced Prediction of Outcomes in Recurrent Exposure and Treatment Settings in Electronic Health Records
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Background
The temporal sequence of clinical events is crucial in outcomes research, yet standard machine learning (ML) approaches often overlook this aspect in electronic health records (EHRs), limiting predictive accuracy.
Methods
We introduce Temporal Learning with Dynamic Range (TLDR), a time-sensitive ML framework, to identify risk factors for post-acute sequelae of SARS-CoV-2 infection (PASC). Using longitudinal EHR data from over 85,000 patients in the Precision PASC Research Cohort (P2RC) from a large integrated academic medical center, we compare TLDR against a conventional atemporal ML model.
Results
TLDR demonstrated superior predictive performance, achieving a mean AUROC of 0.791 compared to 0.668 for the benchmark, marking an 18.4% improvement. Additionally, TLDR’s mean PRAUC of 0.590 significantly outperformed the benchmark’s 0.421, a 40.14% increase. The framework exhibited improved generalizability with a lower mean overfitting index (−0.028), highlighting its robustness. Beyond predictive gains, TLDR’s use of time-stamped features enhanced interpretability, offering a more precise characterization of individual patient records.
Discussion
TLDR effectively captures exposure-outcome associations and offers flexibility in time-stamping strategies to suit diverse clinical research needs.
Conclusion
TLDR provides a simple yet effective approach for integrating dynamic temporal windows into predictive modeling. It is available within the MLHO R package to support further exploration of recurrent treatment and exposure patterns in various clinical settings.
