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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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  9. Natural Language Processing
  11. Exploring Negated Entites For Named Entity Recognition In Italian Lung Cancer Clinical Reports.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Exploring Negated Entites For Named Entity Recognition In Italian Lung Cancer Clinical Reports.

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Exploring Negated Entites for Named Entity Recognition in Italian Lung Cancer Clinical Reports.

Domenico Paolo1, Alessandro Bria2, Carlo Greco3

  • 1Unit of Computer Systems & Bioinformatics, Università Campus Bio-Medico di Roma, Italy.

Studies in Health Technology and Informatics
|May 24, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) using Named Entity Recognition (NER) can extract vital patient data from electronic health records (EHRs). This study demonstrates AI

Keywords:
EHRsNERNSCLCdeep learning

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Area of Science:

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

Background:

  • Electronic Health Records (EHRs) contain valuable patient data for research.
  • Extracting specific clinical information from unstructured text is challenging.
  • Personalized medicine requires efficient data retrieval from clinical notes.

Purpose of the Study:

  • To evaluate the effectiveness of AI-driven Named Entity Recognition (NER) for extracting clinical information from Italian EHRs.
  • To develop and validate a novel set of 29 clinical entities for Non-small cell lung cancer (NSCLC) research.
  • To demonstrate the feasibility of applying AI to Italian clinical text for biomedical research.

Main Methods:

  • Utilized a state-of-the-art AI model pretrained on Italian biomedical texts.
trasformer
  • Applied Named Entity Recognition (NER) to clinical notes.
  • Focused on extracting 29 specific NSCLC-related clinical entities, including negation detection.

    • Main Results:

    • Achieved a promising average F1-score of 80.8% in extracting clinical entities.
    • Demonstrated successful identification of diagnoses, medications, symptoms, and lab tests.
    • Validated the model's performance on Italian clinical notes for NSCLC.

    Conclusions:

    • AI, specifically NER, is effective for extracting crucial biomedical information from Italian EHRs.
    • The developed entity set and AI approach facilitate personalized health research in NSCLC.
    • This work supports the advancement of AI applications in clinical data analysis and future healthcare paradigms.