A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring

Area of Science:

• Artificial Intelligence in Healthcare
• Natural Language Processing (NLP)
• Biomedical Informatics

Background:

• Natural language processing (NLP) is vital for health monitoring, with relation triplet extraction being a key component.
• Current methods face challenges in accurately extracting complex or overlapping information.

Purpose of the Study:

• To propose a novel model for joint entity and relation extraction in health monitoring.
• To enhance the accuracy of overlapping triplet extraction using NLP.

Main Methods:

• A new model combining conditional layer normalization and talking-head attention mechanism.
• Integration of position information to improve extraction accuracy.
• Evaluation on Baidu2019 and CHIP2020 datasets.

Main Results:

• The proposed model effectively extracts overlapping triplets.
• Significant performance improvements were observed compared to baseline methods.
• Enhanced interaction between entity recognition and relation extraction.

Conclusions:

• The novel model demonstrates superior performance in relation triplet extraction for health monitoring.
• This advancement contributes to more accurate and efficient health data analysis.
• The approach shows promise for improving AI-driven health monitoring systems.