The authors report that a BiLSTM-CRF feature extractor combined with XGBoost and logistic regression outperforms several baseline models for diabetes risk prediction on a private Beijing health-check dataset.
Few-shot Name Entity Recognition on StackOverflow
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abstract
StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us. We address this gap by proposing RoBERTa+MAML, a few-shot named entity recognition (NER) method leveraging meta-learning. Our approach, evaluated on the StackOverflow NER corpus (27 entity types), achieves a 5% F1 score improvement over the baseline. We improved the results further domain-specific phrase processing enhance results.
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Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
The authors report that a BiLSTM-CRF feature extractor combined with XGBoost and logistic regression outperforms several baseline models for diabetes risk prediction on a private Beijing health-check dataset.