Vol. 1 No. 4 (2026)

Articles

Temporal Evidence Attribution in Patient Narrative Reconstruction from Fragmented EHR Events

Published 2026-09-05

  • Marieke Bos
    • ,
  • Sanne Kok

Marieke Bos
Department of Physical Therapy, Faculty of Medicine, Leiden University, Leiden, South Holland, Netherlands

Sanne Kok
Department of Physical Therapy, Faculty of Medicine, Leiden University, Leiden, South Holland, Netherlands

Data Science and Computational Intelligence, Volume 1, Issue 4, 2026 cover

Abstract

The widespread adoption of electronic health records has resulted in an unprecedented accumulation of clinical data. However, this data is often highly fragmented, distributed across unstructured clinical notes, structured laboratory results, prescription records, and radiological reports. Clinicians face significant cognitive burdens when attempting to synthesize these disjointed events into a coherent patient history. Recent advancements in natural language generation have proposed automated patient narrative reconstruction to alleviate this burden. Despite these advances, existing models often suffer from a lack of interpretability and fail to accurately attribute generated narrative sentences to their originating source events, a critical requirement for clinical safety. This paper addresses this gap by introducing a comprehensive framework for temporal evidence attribution in patient narrative reconstruction. By mapping fragmented clinical events into a temporally aware dependency graph, the proposed methodology explicitly links generated narrative segments to their underlying source data points. The study provides an extensive analysis of current methodologies, details a novel architecture for semantic and temporal alignment, and evaluates the framework against robust baseline models. The findings demonstrate significant improvements in both narrative coherence and clinical verifiability. This research contributes to the broader domain of medical informatics by providing a foundation for transparent, evidence-based automated clinical summarization, ultimately supporting safer and more efficient clinical decision-making.

Keywords

Electronic Health Records, Narrative Reconstruction, Temporal Reasoning, Evidence Attribution, Clinical Informatics

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