Leveraging Temporal Learning with Dynamic Range (TLDR) for Enhanced Prediction of Outcomes in Recurrent Exposure and Treatment Settings in Electronic Health Records

BackgroundThe 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. MethodsWe 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. ResultsTLDR demonstrated superior predictive performance, achieving a mean AUROC of 0.791 compared to 0.668 for the benchmark, marking an 18.4% improvement. Additionally, TLDRs mean PRAUC of 0.590 significantly outperformed the benchmarks 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, TLDRs use of time-stamped features enhanced interpretability, offering a more precise characterization of individual patient records. DiscussionTLDR effectively captures exposure-outcome associations and offers flexibility in time-stamping strategies to suit diverse clinical research needs. ConclusionTLDR 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.