Bayesian Dose Optimization

Bayesian Dose Optimization is a Bayesian inference-based clinical decision-support method that integrates patient-specific pharmacokinetic data with prior population estimates to dynamically individualize drug dosage regimens for optimized therapeutic outcomes.

56

Publications

2.5K

Citations

244

Authors

119

Institutions

Publications per year

2017–2026

17

Variants

Bayesian Individualized Dosing

Authors

244

  1. YY
    Ying Yuan

    The University of Texas MD Anderson Cancer Center, United States

    7 publications · 436 citations

    7

    h-index

  2. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    7 publications · 330 citations

    7

    h-index

  3. SZ
    S. Zacks

    Binghamton University, United States

    4 publications · 120 citations

    4

    h-index

  4. HQ
    Hoang Q. Nguyen

    The University of Texas MD Anderson Cancer Center, United States

    4 publications · 233 citations

    4

    h-index

  5. MN
    Michael Neely

    University of Southern California, United States

    3 publications · 180 citations

    3

    h-index

1–5 of 244

Venues

  1. 6

    h-index

  2. Statistics in Medicine

    4 publications · 188 citations

    4

    h-index

  3. 4

    h-index

  4. Biometrics

    3 publications · 267 citations

    3

    h-index

  5. Therapeutic Drug Monitoring

    3 publications · 61 citations

    3

    h-index

1–5