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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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[Automatic ICD-10 coding : Natural language processing for German MRI reports].

Andreas Mittermeier1,2, Matthias Aßenmacher3, Balthasar Schachtner4,5

  • 1Klinik und Poliklinik für Radiologie, LMU Klinikum, LMU München, München, Deutschland. Andreas.Mittermeier@med.uni-muenchen.de.

Radiologie (Heidelberg, Germany)
|August 9, 2024
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) models can accurately predict ICD-10 codes for German radiology reports. The flanT5 model shows strong performance, acting as a valuable assistant for medical coders.

Keywords:
Artificial intelligenceLanguage modelsMedical codingNatural language processingRadiology reports

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Radiology

Context:

  • Medical coding of radiology reports is crucial for quality care and billing.
  • This process is complex and prone to errors.
  • Accurate coding requires efficient and reliable methods.

Purpose:

  • To evaluate the effectiveness of natural language processing (NLP) for International Classification of Diseases, 10th Revision (ICD-10) coding of German radiology reports.
  • To assess the performance of fine-tuned language models, specifically GermanBERT and flanT5.

Summary:

  • A retrospective study analyzed 100,672 German magnetic resonance imaging (MRI) radiology reports (2010-2020).
  • Fine-tuned GermanBERT and flanT5 models were trained on full and reduced datasets of ICD-10 codes.
  • flanT5 demonstrated superior performance, achieving nearly 70% top-3 accuracy on the full dataset, especially when combined with report metadata.

Impact:

  • Fine-tuned language models can reliably predict ICD-10 codes for German MRI radiology reports.
  • The flanT5 model shows potential as a medical coding assistant, aiding coders and reducing workload.
  • This technology can improve the efficiency and accuracy of medical coding in radiology.