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Supplementary Materials Data Availability Statement The datasets used and/or analyzed during the current study are available as MS Excel files (.xlsx) and RevMan file (.rm5) from the corresponding author upon reasonable request.
This study presents an NLP-based framework that utilizes ClinicalBERT embeddings to represent ICD-10 diagnostic codes, effectively overcoming the limitations of traditional encoding methods and improving the predictive accuracy of medical AI models in cardiovascular event prediction
Most peptides are degraded rapidly by gastric acid and intestinal proteases
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