Development and external validation of a machine learning model to predict the initial dose of vancomycin for targeting an area under the concentration-time curve of 400-600 mg∙h/L.

Journal: International journal of medical informatics
PMID: 39893767

Abstract

PURPOSE: To develop and validate a novel artificial intelligence model for predicting the initial empiric dose of vancomycin, with the aim of achieving an area under the concentration-time curve (AUC) of 400-600 mg∙h/L, using individual clinical data.

Authors

  • Yun Woo Lee
    Division of Infectious Diseases, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea.
  • Ji-Hun Kim
    Department of Emergency Medicine, The Catholic University of Korea, Uijeongbu St. Mary's Hospital, Uijeongbu, Gyeonggi-do, Republic of Korea.
  • Jin Ju Park
    Division of Infectious Diseases, Department of Internal Medicine, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.
  • Hyejin Park
    Yongin in silico Medical Research Centre, Syntekabio Inc., 283 Dongbaekjungang-ro, C508, Giheung-gu, Yongin, Gyeonggi-do, 17006, South Korea.
  • Hyeonji Seo
    Division of Infectious Diseases, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea.
  • Yong Kyun Kim
    Division of Infectious Diseases, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea. Electronic address: amoureuxyk@hallym.or.kr.

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