  Sepsis is a life-threatening condition caused by the body's response to an
infection. In order to treat patients with sepsis, physicians must control
varying dosages of various antibiotics, fluids, and vasopressors based on a
large number of variables in an emergency setting. In this project we employ a
"world model" methodology to create a simulator that aims to predict the next
state of a patient given a current state and treatment action. In doing so, we
hope our simulator learns from a latent and less noisy representation of the
EHR data. Using historical sepsis patient records from the MIMIC dataset, our
method creates an OpenAI Gym simulator that leverages a Variational
Auto-Encoder and a Mixture Density Network combined with a RNN (MDN-RNN) to
model the trajectory of any sepsis patient in the hospital. To reduce the
effects of noise, we sample from a generated distribution of next steps during
simulation and have the option of introducing uncertainty into our simulator by
controlling the "temperature" variable. It is worth noting that we do not have
access to the ground truth for the best policy because we can only evaluate
learned policies by real-world experimentation or expert feedback. Instead, we
aim to study our simulator model's performance by evaluating the similarity
between our environment's rollouts with the real EHR data and assessing its
viability for learning a realistic policy for sepsis treatment using Deep
Q-Learning.
