Consistent Relexicalization of Clinical Documents using Graph-Based Approach

Clinical time-series data are difficult to model with methods designed for regular sequences because they exhibit irregular sampling, frequent missing values, and heterogeneous observation patterns across variables. Existing approaches commonly use observation masks and time-gap information, but they do not continuously capture the decaying reliability of past observations or consistently organize multi-resolution information within a coherent temporal context during aggregation. To address these limitations, we propose Reliability-aware Temporal Aggregation with Mamba (ReTAMamba), which reconstructs clinical time series as time-variable token sequences, estimates observation reliability from missingness and elapsed time, and augments interval summaries with statistical descriptors. Chronological Weaving is used to integrate short- and long-term temporal information within a coherent temporal context, and a budgeted token router is applied to constrain sequence length while preserving informative summaries. Experiments on MIMIC-IV, eICU, and PhysioNet 2012 show that ReTAMamba consistently improves AUPRC over strong baselines, with average relative gains of 7.51%, 7.80%, and 10.15%, respectively. Cohort-level and patient-level analyses on eICU further showed that the learned mean decay for more dynamic signals, such as heart rate and blood pressure, was 24.3% larger than that for relatively static signals, such as laboratory test variables. These findings suggest that effective prediction in irregular clinical time series requires modeling not only what was measured, but also when and how it was observed, including information freshness and observation timeliness.

Clinical language models increasingly operate over electronic health records (EHRs), yet patient records are not stored as temporally grounded trajectories. Clinical notes describe symptoms, assessments, and disease progression, but often compress or narratively reorder events. Structured EHR rows provide timestamps for labs, medications, vitals, and procedures, but capture only part of the clinical story. We formulate clinical timeline reconstruction as retrieval-augmented temporal grounding: constructing a patient trajectory by using narrative text for event semantics and structured rows as partial temporal evidence. We introduce a scaffolded workflow that extracts central narrative events, builds an initial temporal scaffold, attaches non-central events, and calibrates timestamps using retrieved structured EHR rows. We evaluate on 40 discharge summaries, including 15 i2b2-derived and 25 MIMIC-IV summaries, each with manual gold-standard timelines and aligned structured EHR data. Across models, multimodal calibration left event match rates largely unchanged and generally improved temporal performance: mean paired case-level multimodal-unimodal differences were positive in 7 of 12 model-metric comparisons across concordance and AULTC, with none negative. However, uncertainty was substantial given the 40-case sample; paired case-level bootstrap intervals excluded zero only for the DeepSeek V3.2 AULTC improvement. A gap analysis shows that 35.1% of text-derived events have no structured counterpart. These findings support treating structured EHR data as partial temporal evidence for narrative-derived patient trajectories.

Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA. While manual de-identification remains the gold standard, it is costly and slow, motivating the need for automated methods that combine privacy guarantees with high utility. Most automated text de-identification pipelines employed named entity recognition (NER) to identify protected entities for redaction. Although methods based on differential privacy (DP) provide formal privacy guarantees, more recently also large language models (LLMs) are increasingly used for text de-identification in the clinical domain. In this work, we present the first comparative study of DP, NER, and LLMs for Dutch clinical text de-identification. We investigate these methods separately as well as hybrid strategies that apply NER or LLM preprocessing prior to DP, and assess performance in terms of privacy leakage and extrinsic evaluation (entity and relation classification). We show that DP mechanisms alone degrade utility substantially, but combining them with linguistic preprocessing, especially LLM-based redaction, significantly improves the privacy-utility trade-off.