Semantic NLP Pipelines for Interoperable Patient Digital Twins from Unstructured EHRs

2026-01-09

arXiv.org

AI Summary

This work addresses the challenge of constructing interoperable patient digital twins from unstructured electronic health records (EHRs), which is hindered by clinical text heterogeneity and the lack of standardized mappings. The authors propose the first end-to-end semantic natural language processing (NLP) pipeline that tightly integrates with the Fast Healthcare Interoperability Resources (FHIR) standard. By combining named entity recognition, concept normalization to SNOMED-CT and ICD-10 terminologies, and relation extraction, the pipeline automatically transforms free-text clinical notes into structured FHIR resources. Evaluated on the MIMIC-IV Clinical Database Demo, the approach significantly improves F1 scores for both entity and relation extraction, outperforms baseline methods in schema completeness and system interoperability, and enables the automated construction of patient digital twins with high semantic consistency.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Digital twins -- virtual replicas of physical entities -- are gaining traction in healthcare for personalized monitoring, predictive modeling, and clinical decision support. However, generating interoperable patient digital twins from unstructured electronic health records (EHRs) remains challenging due to variability in clinical documentation and lack of standardized mappings. This paper presents a semantic NLP-driven pipeline that transforms free-text EHR notes into FHIR-compliant digital twin representations. The pipeline leverages named entity recognition (NER) to extract clinical concepts, concept normalization to map entities to SNOMED-CT or ICD-10, and relation extraction to capture structured associations between conditions, medications, and observations. Evaluation on MIMIC-IV Clinical Database Demo with validation against MIMIC-IV-on-FHIR reference mappings demonstrates high F1-scores for entity and relation extraction, with improved schema completeness and interoperability compared to baseline methods.

Problem

• digital twins
• unstructured EHRs
• interoperability
• semantic NLP
• FHIR

Innovation

• semantic NLP pipeline
• patient digital twin
• FHIR interoperability
• clinical concept normalization
• relation extraction