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This research presents a novel method for predicting heart failure outcomes using short-context autoregressive Transformers, demonstrating that compact models can outperform traditional baselines like XGBoost and BERT on large clinical datasets. By leveraging Llama backbones with specific architectural tweaks such as category-level tokenization, the study offers an efficient approach to risk stratification without requiring massive context windows typically associated with foundation models. The findings suggest significant potential for deploying lightweight AI systems in real-world healthcare settings where computational resources may be constrained but accurate patient prognostication is critical.
Trajectory-guided discharge stratification for heart failure using short-context electronic health record sequence modeling
arXiv:2511.16839v4 Announce Type: replace Abstract: Purpose: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinely collected electronic health records (EHRs) remains challenging. Methods: We develop trajectory-guided discharge stratification for heart failure (TGDS-HF), a methodology that reads the patient in-hospital trajectory of diagnoses, vital signs, laboratories, medications, and procedures end-to-end with a compact short-context autoregressive Transformer, and uses it to stratify one-year risks of clinical instability (a rehospitalization phenotype) or mortality for discharge care. We instantiate TGDS-HF on a Swedish HF cohort (N = 42,820) to predict one-year clinical instability or mortality at the initial HF diagnosis in-hospital. TGDS-HF has three components: category-level tokenization, recency-weighted temporal representation, and sequence model configuration. We run ablations on these components to show the effectiveness of TGDS-HF. Results: Against traditional eXtreme gradient boosting machine (XGBoost) and bidirectional encoder representations from Transformers (BERT)-based EHR sequence-modeling baselines, TGDS-HF (Llama backbone) achieved area under the precision-recall curves (AUPRCs) with 95% confidence intervals of 0.555 (0.535-0.575) and 0.574 (0.550-0.599) across the two tasks at the method default, with robust calibration. A task-specific refinement using daily aggregation of repeated continuous events improves the mortality task to 0.582 (0.558-0.608). Further, TGDS-HF maintains strong performance under reduced clinical concept availability and limited training data. Conclusion: Combined predictions of instability and mortality from TGDS-HF may support personalized discharge planning, ranging from follow-up in primary care to specialist-led management and, when appropriate, palliative care.
Abstract:Purpose: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinely collected electronic health records (EHRs) remains challenging. Methods: We develop trajectory-guided discharge stratification for heart failure (TGDS-HF), a methodology that reads the patient in-hospital trajectory of diagnoses, vital signs, laboratories, medications, and procedures end-to-end with a compact short-context autoregressive Transformer, and uses it to stratify one-year risks of clinical instability (a rehospitalization phenotype) or mortality for discharge care. We instantiate TGDS-HF on a Swedish HF cohort (N = 42,820) to predict one-year clinical instability or mortality at the initial HF diagnosis in-hospital. TGDS-HF has three components: category-level tokenization, recency-weighted temporal representation, and sequence model configuration. We run ablations on these components to show the effectiveness of TGDS-HF. Results: Against traditional eXtreme gradient boosting machine (XGBoost) and bidirectional encoder representations from Transformers (BERT)-based EHR sequence-modeling baselines, TGDS-HF (Llama backbone) achieved area under the precision-recall curves (AUPRCs) with 95% confidence intervals of 0.555 (0.535-0.575) and 0.574 (0.550-0.599) across the two tasks at the method default, with robust calibration. A task-specific refinement using daily aggregation of repeated continuous events improves the mortality task to 0.582 (0.558-0.608). Further, TGDS-HF maintains strong performance under reduced clinical concept availability and limited training data. Conclusion: Combined predictions of instability and mortality from TGDS-HF may support personalized discharge planning, ranging from follow-up in primary care to specialist-led management and, when appropriate, palliative care.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2511.16839 [cs.LG]
(or arXiv:2511.16839v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2511.16839 Focus to learn more arXiv-issued DOI via DataCite
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