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Sepsis is a life-threatening condition caused by the body's response to aninfection. In order to treat patients with sepsis, physicians must controlvarying dosages of various antibiotics, fluids, and vasopressors based on alarge 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 nextstate of a patient given a current state and treatment action. In doing so, wehope our simulator learns from a latent and less noisy representation of theEHR data. Using historical sepsis patient records from the MIMIC dataset, ourmethod creates an OpenAI Gym simulator that leverages a VariationalAuto-Encoder and a Mixture Density Network combined with a RNN (MDN-RNN) tomodel the trajectory of any sepsis patient in the hospital. To reduce theeffects of noise, we sample from a generated distribution of next steps duringsimulation and have the option of introducing uncertainty into our simulator bycontrolling the "temperature" variable. It is worth noting that we do not haveaccess to the ground truth for the best policy because we can only evaluatelearned policies by real-world experimentation or expert feedback. Instead, weaim to study our simulator model's performance by evaluating the similaritybetween our environment's rollouts with the real EHR data and assessing itsviability for learning a realistic policy for sepsis treatment using DeepQ-Learning.

Sepsis World Model: A MIMIC-based OpenAI Gym "World Model" Simulator for Sepsis Treatment