arXiv subjects

Erik Aerts

Publications and source records attributed to Erik Aerts.

2 recordsLinked to original sources

arXiv2026

AI-based detection of worsening heart failure from low-resolution telemonitoring data

Objective: Heart failure (HF) presents a healthcare challenge due to its high comorbidity burden, aging patient population and frequent hospitalizations. Remote monitoring offers a promising approach to managing HF patients by early detection of health deterioration. Developing autonomous systems to detect signs of worsening in telemonitoring data is of interest to reduce the workload of healthcare personnel. Methods: We propose the TRACER model, a Transformer with Contrastive Event Representation, designed to predict timelines leading to rare hospitalization events in low-resolution and irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings for each biomarker, contrastive pre-training to enhance anomaly detection via representation learning, and independent binary classifiers for detection. We used measurement data containing remotely recorded biomarker sequences from 276 HF patients segmented into overlapping windows based on temporal rules, and labeled the windows based on the occurrence of HF relevant hospitalizations at the latter edge of the window. Results: TRACER was able to correctly predict 66.7% timelines leading up to HF hospitalizations in the highly imbalanced real-world dataset with an overestimation of 7.9%. Reformulating the training of TRACER as an event detection problem improved the predictive performance compared with training directly on forecasting windows, enabling more effective use of the limited hospitalization events. Conclusion: TRACER demonstrated superior performance in detecting signs of worsening status in real-world telemonitoring data compared to the other tested models. Significance: TRACER shows promise in identifying signs of clinical deterioration that allow for alerts to be generated to provide counteractive treatment in patients with HF.

cs.AI↗

arXiv2026

Trajectory-guided discharge stratification for heart failure using short-context electronic health record sequence modeling

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.

cs.LG↗