Clinical Report: Utilizing Large Language Models for Early Detection of OSAHS
Overview
This study presents OSAHSrisk-LLM, a large language model framework designed to identify risk factors associated with obstructive sleep apnea hypopnea syndrome (OSAHS) from patient-generated text. The framework achieved a 92.9% accuracy in classifying text related to OSAHS risk factors, outperforming traditional models.
Background
Obstructive sleep apnea hypopnea syndrome (OSAHS) is a prevalent condition that often goes undiagnosed, leading to significant health risks. Early detection of risk factors is crucial for timely intervention and management. Traditional methods of risk assessment may overlook valuable insights found in unstructured patient narratives, necessitating innovative approaches for effective screening.
Data Highlights
OSAHSrisk-LLM achieved an overall accuracy of 92.9% in identifying OSAHS-related risk factors from patient-generated text.
Key Findings
OSAHSrisk-LLM outperformed baseline models including CNN, Text-CNN, Transformer, and BERT.
The framework effectively handled highly imbalanced class distributions.
It utilized a relevance-aware and ontology-constrained reasoning strategy for text analysis.
Standardized clinical terms were used to normalize extracted concepts from informal narratives.
Further validation against clinically confirmed OSAHS diagnoses is required for real-world application.
Clinical Implications
The integration of large language models in clinical settings can enhance the early detection of OSAHS by analyzing patient narratives. This approach may improve screening efficiency and facilitate timely interventions, ultimately reducing the burden of undiagnosed OSAHS.
Conclusion
The findings indicate that large language models can significantly aid in identifying OSAHS-related risk factors from unstructured text. Further research is needed to validate these results in clinical practice.
