Deep continual learning for medical call incidents text classification under the presence of dataset shifts
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View abstract on PubMed
Summary
This summary is machine-generated.This study shows continual learning (CL) effectively adapts deep learning models for prioritizing emergency medical calls. CL techniques significantly improve model performance despite changing data distributions over time.
Area Of Science
- Artificial Intelligence
- Machine Learning
- Medical Informatics
Background
- Emergency Medical Call Incidents (EMCI) require accurate prioritization.
- Dataset shifts can degrade the performance of deep learning models over time.
- Prior research has not extensively explored Continual Learning (CL) for EMCI prioritization using real-world data.
Purpose Of The Study
- To develop and evaluate deep learning classifiers for prioritizing EMCI based on life-threatening levels.
- To assess the impact of dataset shifts on model performance.
- To investigate the effectiveness of various CL strategies in mitigating performance degradation due to dataset shifts.
Main Methods
- Utilized a large dataset of 1,982,746 EMCI instances from Valencia, Spain (2009-2019, excluding 2013).
- Analyzed dataset shifts including prior probability, covariate, and concept shifts.
- Implemented and compared four deep CL strategies (cumulative learning, continual fine-tuning, experience replay, synaptic intelligence) and three baselines (joint training, static approach, single fine-tuning) using DistilBERT models.
Main Results
- Confirmed the presence of prior probability, covariate, and concept shifts in the EMCI dataset.
- Demonstrated statistically significant improvements in F1-score for CL techniques compared to non-continual approaches, indicating enhanced backward and forward knowledge transfer.
- CL strategies effectively adapted deep learning classifiers to evolving data distributions, maintaining stable performance.
Conclusions
- Continual Learning techniques are effective for adapting deep learning models to dynamic data distributions in EMCI prioritization.
- CL ensures the stability and reliability of predictive models for emergency medical services over time.
- This study is the first to apply CL approaches to real-world EMCI data, offering a novel perspective on improving emergency response systems.
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