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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Related Experiment Video

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports

Dorian Culié1,2, Renaud Schiappa2, Sara Contu2

  • 1Cervico-Facial Oncology Surgical Department, University Institute of Face and Neck, Centre Antoine Lacassagne University of Côte d'Azur, Nice, France.

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|December 10, 2024
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Summary
This summary is machine-generated.

This study developed an automated system using artificial intelligence to extract key data from thyroid cancer patient records. This improves the assessment of malignancy risk and supports comprehensive data warehousing.

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Thyroid nodules are prevalent, necessitating accurate malignancy risk assessment for patient care.
  • Surgical intervention is the definitive treatment for indeterminate thyroid nodules.
  • Efficient data extraction from electronic health records (EHRs) is vital for improving initial assessments.

Purpose of the Study:

  • To develop an automated process for extracting and structuring biomedical insights from EHRs.
  • To apply convolutional neural networks (CNNs) to a large thyroid pathology cohort.
  • To enhance the initial assessment of malignancy risk in thyroid nodules.

Main Methods:

  • A cohort of 1,600 patients with thyroid pathology was used for model development and testing.
  • Electronic health records from various clinical encounters were utilized.
  • A CNN-based model (RUBY-THYRO) was developed using SpaCy, keyword extraction, and postprocessing.

Main Results:

  • The automated system demonstrated high performance, with most variables (30/42) exceeding 90% in precision, recall, and F1 score.
  • Pathologic tumor stage achieved 100% accuracy, while the number of nodules had lower scores.
  • Surgical and preanesthesia reports yielded particularly high performance metrics.

Conclusions:

  • A CNN-based natural language processing (NLP) approach effectively extracts and structures data from thyroid pathology EHRs.
  • AI-driven NLP offers a cost-effective method for extensive data extraction.
  • This facilitates the creation of comprehensive hospital-wide data warehouses for thyroid pathology.