A standard Transformer with sigmoid multi-label classification is reported to reach 77.8% accuracy on MIMIC-IV disease prediction, but the evaluation is not reproducible and the baselines are not comparable.
Neural natural language processing for unstructured data in electronic health records: a review,
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Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction
A standard Transformer with sigmoid multi-label classification is reported to reach 77.8% accuracy on MIMIC-IV disease prediction, but the evaluation is not reproducible and the baselines are not comparable.