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Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study.

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Summary
This summary is machine-generated.

This study introduces a novel graph-based model for extracting family history information from clinical text, achieving top performance in a challenge. The system effectively extracts family entities and their relationships, improving disease diagnosis support.

Keywords:
deep biaffine attentionfamily history informationnamed entity recognitionrelation extraction

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Biomedical Natural Language Processing

Background:

  • Family history is crucial for disease diagnosis and treatment.
  • Extracting family history from electronic health records (EHRs) is challenging due to complex information.
  • Key challenges include named entity recognition (NER) and relation extraction (RE) of family information.

Purpose of the Study:

  • To introduce a system for joint entity and relation extraction of family history from clinical text.
  • To address the 2019 n2c2/OHNLP track on family history extraction.
  • To develop a robust method for understanding familial health data in EHRs.

Main Methods:

  • A novel graph-based model with biaffine attention was proposed.
  • NER and RE were unified within a graph representation.
  • Convolutional Neural Network (CNN)-Bidirectional Long Short-Term Memory (BiLSTM) and BERT were used for encoding, with a biaffine classifier for extraction.

Main Results:

  • The system achieved first place in the 2019 n2c2/OHNLP challenge.
  • The best F1 scores were 0.8745 for NER and 0.6810 for RE.
  • Further fine-tuning improved F1 scores to 0.8823 for NER and 0.7048 for RE.

Conclusions:

  • The proposed graph-based model effectively extracts family history information from clinical text.
  • The system demonstrates strong performance in both entity and relation extraction tasks.
  • This approach holds promise for improving the utility of EHR data in clinical practice.