PRTA:Joint extraction of medical nested entities and overlapping relation via parameter sharing progressive recognition and targeted assignment decoding scheme

Bowen Liu ¹, Hong Song ², Yucong Lin ³

⁰College of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.

Computers in Biology and Medicine +

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May 10, 2024

View abstract on PubMed

Summary

This study introduces a novel joint model for medical information extraction, improving consistency in analyzing electronic medical records. The PRTA model effectively handles nested entities and overlapping relationships, outperforming existing methods.

Area Of Science

Natural Language Processing
Medical Informatics
Machine Learning

Background

Analyzing electronic medical records involves entity and relationship extraction, often treated separately.
Existing models lack consistency due to isolated processing of nested entities and relationships.
This separation hinders comprehensive information extraction from medical texts.

Purpose Of The Study

To propose a joint medical entity-relation extraction model for improved consistency.
To develop a model that simultaneously processes nested entities and relationships.
To enhance the accuracy of information extraction from electronic medical records.

Main Methods

A joint model named Progressive Recognition and Targeted Assignment (PRTA) was developed.
PRTA utilizes shared information from sequence and word embedding layers for simultaneous training.
Novel strategies include a compound triangle for nested entity recognition and adaptive multi-space interaction for relationship extraction.

Main Results

The PRTA model demonstrated superior performance over state-of-the-art methods on multiple datasets.
The method effectively addresses challenges posed by nested entities and overlapping relationships.
Experiments were conducted on the Private Liver Disease Dataset (PLDD) and public datasets (NYT, ACE04, ACE05).

Conclusions

The proposed joint model significantly improves medical information extraction accuracy.
PRTA offers a robust solution for handling complex entity and relation structures in medical records.
This approach enhances the utility of electronic medical records for clinical analysis and research.

Keywords:

Joint entity-relation extraction Nested entity Overlapping relation Parameter sharing Progressive recognition