Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM
  Framework with Mortality Classifier and Transformer
Dipesh Tamboli Jiayu Chen Kiran Pranesh Jotheeswaran Denny Yu Vaneet Aggarwal
Sepsis, a life-threatening condition triggered by the body's exaggerated
response to infection, demands urgent intervention to prevent severe
complications. Existing machine learning methods for managing sepsis struggle
in offline scenarios, exhibiting suboptimal performance with survival rates
below 50%. This paper introduces the POSNEGDM -- ``Reinforcement Learning with
Positive and Negative Demonstrations for Sequential Decision-Making" framework
utilizing an innovative transformer-based model and a feedback reinforcer to
replicate expert actions while considering individual patient characteristics.
A mortality classifier with 96.7\% accuracy guides treatment decisions towards
positive outcomes. The POSNEGDM framework significantly improves patient
survival, saving 97.39% of patients, outperforming established machine learning
algorithms (Decision Transformer and Behavioral Cloning) with survival rates of
33.4% and 43.5%, respectively. Additionally, ablation studies underscore the
critical role of the transformer-based decision maker and the integration of a
mortality classifier in enhancing overall survival rates. In summary, our
proposed approach presents a promising avenue for enhancing sepsis treatment
outcomes, contributing to improved patient care and reduced healthcare costs.