DT-Transformer predicts next disease events with median age- and sex-stratified AUC 0.871 across 896 categories on held-out and prospective data from a 1.7M-patient multi-hospital EHR dataset.
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Mistral uses careful lexical simplification to raise readability while keeping BERTScore at 0.91 comparable to humans, whereas QWen improves readability but shows a disconnect with its 0.89 BERTScore in biomedical text simplification.
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DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System
DT-Transformer predicts next disease events with median age- and sex-stratified AUC 0.871 across 896 categories on held-out and prospective data from a 1.7M-patient multi-hospital EHR dataset.
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Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification
Mistral uses careful lexical simplification to raise readability while keeping BERTScore at 0.91 comparable to humans, whereas QWen improves readability but shows a disconnect with its 0.89 BERTScore in biomedical text simplification.