Active learning pipeline to automatically identify candidate terms for a CDSS ontology: measures, experiments, and performance
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Summary
This summary is machine-generated.This study introduces an active learning framework to automate key phrase identification for Clinical Decision Support System (CDSS) ontologies. This approach reduces manual effort in ontology development and maintenance.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Healthcare
- Natural Language Processing for Ontologies
Background
- Clinical Decision Support System (CDSS) ontologies are crucial for standardizing medical vocabulary and data integration.
- Traditional ontology development is manual, time-consuming, and requires extensive domain expertise.
- Automating key phrase identification for CDSS ontologies can significantly improve efficiency.
Purpose Of The Study
- To present an active learning framework for automatic identification of key phrases relevant to CDSS ontologies.
- To reduce the manual labor and domain expertise required for ontology development and long-term maintenance.
- To improve the accuracy and efficiency of CDSS ontology creation through automated methods.
Main Methods
- A BiLSTM CRF model integrated into a human-in-the-loop active learning pipeline.
- Uncertainty sampling strategy for document selection, prioritizing low-confidence model instances for human review.
- Novel uncertainty aggregation methods (KPSum, KPAvg, DOCSum, DOCAvg) combined with uncertainty measures (MTP, TE, Margin) for document-level confidence scoring.
Main Results
- The proposed active learning framework effectively identifies key phrases for CDSS ontologies.
- New uncertainty aggregation methods enhance the document selection process in active learning.
- The approach leads to more transparent, replicable, and efficient prioritization of documents for annotation.
Conclusions
- Active learning significantly facilitates CDSS ontology development by reducing manual effort.
- The framework supports human experts, particularly in the long-term maintenance of ontologies.
- Automated key phrase identification is a valuable strategy for efficient and accurate ontology engineering.
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