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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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[Extraction of features from clinical routine data using text mining].

Bastian Grundel1, Marc-Antoine Bernardeau1, Holger Langner2

  • 1Klinik und Poliklinik für Augenheilkunde, Universitätsmedizin Greifswald, Greifswald, Deutschland.

Der Ophthalmologe : Zeitschrift Der Deutschen Ophthalmologischen Gesellschaft
|July 30, 2020
PubMed
Summary
This summary is machine-generated.

Automated text mining effectively extracts valuable clinical data from discharge letters for macular disease patients. This method accurately retrieves information like visual acuity and diagnoses, aiding treatment prediction.

Keywords:
Decision support systemsElectronic health recordsMacular degenerationNatural language processingSystematized nomenclature of medicine

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Natural Language Processing

Background:

  • Anti-VEGF drugs are standard treatments for macular diseases, generating extensive patient data.
  • Clinical data crucial for understanding treatment courses is often unstructured, existing only as free text in medical records.

Purpose of the Study:

  • To evaluate the efficacy of automated text mining for extracting interpretable information from clinical texts.
  • To assess the suitability of a customized text mining approach for analyzing patient data in discharge letters.

Main Methods:

  • A retrospective analysis of 40,485 discharge letters from 3683 patients treated for macular diseases.
  • Utilized the Averbis Health Discovery text mining platform, enhanced with a German medical vocabulary (SNOMED CT).
  • Validated extracted data (visual acuity, intraocular pressure, diagnoses) against structured database entries.

Main Results:

  • High agreement rates were achieved: 94.7% for visual acuity, 98.3% for intraocular pressure, and 94.7% for diagnoses.
  • Manual review identified discrepancies in 50% of non-matching cases due to various reasons.
  • Adjustments to the text mining process led to a 1-3% improvement in F1 values.

Conclusions:

  • Text mining is highly suitable for extracting structured content from clinical discharge letters.
  • Automated extraction facilitates further evaluation and understanding of patient treatment courses.
  • The developed text mining method demonstrates strong performance in analyzing ophthalmological clinical texts.