Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach

@article{Nemati2016OptimalMD,
  title={Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach},
  author={Shamim Nemati and Mohammad M. Ghassemi and Gari D. Clifford},
  journal={2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2016},
  pages={2978-2981}
}
Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a clinician-in-the-loop sequential decision making framework, which provides an individualized dosing policy adapted to each patient's evolving clinical phenotype. We employed retrospective data from the publicly available MIMIC II intensive care unit database, and developed a deep… CONTINUE READING