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This study introduces a system using natural language processing and SAP HANA to structure free-text patient data from electronic health records. This enables better data querying and supports clinical research applications.

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Database Technology

Background:

  • Electronic patient records contain significant amounts of unstructured free text.
  • Current data structuring and coding are often limited, hindering data utilization.
  • Extracting meaningful information from clinical notes is a major challenge.

Purpose of the Study:

  • To develop a system for transforming unstructured clinical text into a structured, semantically explicit format.
  • To leverage in-memory database technology for efficient clinical data warehousing and analysis.
  • To enable advanced querying and information extraction from patient records.

Main Methods:

  • Utilized SAP HANA in-memory database technology and the SAP Connected Health platform.
  • Implemented a natural language processing (NLP) pipeline to analyze unstructured text.
  • Mapped analyzed text to a standardized vocabulary within a defined information model.

Main Results:

  • Successfully created semantically standardized patient profiles from free-text data.
  • Demonstrated the capability to integrate and warehouse clinical data effectively.
  • Established a foundation for diverse clinical and research applications.

Conclusions:

  • The developed system effectively structures unstructured patient data, enhancing its usability.
  • SAP HANA and NLP integration provide a robust solution for clinical data challenges.
  • Semantically standardized patient profiles facilitate improved clinical decision-making and research.