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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., Comment: This project was done as a class project for CS221 at Stanford University

In this work, we study abstractive text summarization by exploring different models such as LSTM-encoder-decoder with attention, pointer-generator networks, coverage mechanisms, and transformers. Upon extensive and careful hyperparameter tuning we compare the proposed architectures against each other for the abstractive text summarization task. Finally, as an extension of our work, we apply our text summarization model as a feature extractor for a fake news detection task where the news articles prior to classification will be summarized and the results are compared against the classification using only the original news text. keywords: LSTM, encoder-deconder, abstractive text summarization, pointer-generator, coverage mechanism, transformers, fake news detection