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Features included patient demographics, comorbidities, vital signs, laboratory measurements, medications, and vancomycin dosing information. The model architecture combined Long Short\u2010Term Memory and Multi\u2010Head Attention layers, supplemented with skip connections to incorporate past dosage information at the final layer of the deep learning model. Model performance was evaluated using mean absolute error (MAE) and root mean square error (RMSE) metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 2205 encounters met the eligibility criteria. The median age was 57\u2009years, and the median ICU length of stay was 4.9\u2009days. The model achieved an MAE of 3.15\u2009mg\/L and an RMSE of 4.17\u2009mg\/L, comparable to that of a critical care pharmacist aided by a Bayesian dosing software. Additionally, deviations from patient\u2010specific model\u2010based dose recommendations were generally associated with nontherapeutic vancomycin levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the potential to leverage deep learning to individualize and support vancomycin therapeutic drug monitoring in critically ill patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/phar.70062","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T12:59:51Z","timestamp":1759237191000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Nephrocast\u2010V: A Deep Learning Model for the Prediction of Vancomycin Trough Concentration Using Electronic Health Record 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