Papers with ATC
Zero-Shot ATC Coding with Large Language Models for Clinical Assessments (2025.naacl-industry)
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| Challenge: | Manual assignment of ATC codes to prescription records is a significant bottleneck in healthcare operations. |
| Approach: | They propose a method to automate the assignment of ATC codes to prescription records . they use locally deployable large language models to guide LLMs through the ontology . |
| Outcome: | The proposed method achieves 78% exact match accuracy with GPT-4o and 60% with Llama 3.1 70B. |
On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction (2021.findings-acl)
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| Challenge: | Neural network algorithms excel on Automatic Text Classification tasks, but they are expensive and require high computational costs. |
| Approach: | They propose to exploit the cost-effectiveness of stacking of automatic text classification classifiers to improve their effectiveness. |
| Outcome: | The proposed method can predict the best ensemble in each scenario using only fraction of available training data. |
Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification (2025.acl-long)
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Guilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Chaves Dutra Da Rocha
| Challenge: | Existing methods to mitigate class imbalanced datasets are limited by existing methods. |
| Approach: | They propose two undersampling methods inspired by state-of-the-art Instance Selection techniques to mitigate class imbalance bias in ATC. |
| Outcome: | The proposed methods reduce classifier bias (56%) across all datasets without effectiveness loss while improving efficiency (1.6x speedup), scalability and reducing carbon emissions (up to 50%). |