The goal of this PhD was to generate novel tools to improve the management of patients with sepsis, by applying machine learning techniques on routinely collected electronic health records. Machine learning is an application of artificial intelligence (AI), where a machine analyses data and becomes able to execute complex tasks without being explicitly programmed. Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients. This represents a key clinical challenge and a top research priority.
The main contribution of the research has been the development of a reinforcement learning framework and algorithms, in order to tackle this sequential decision-making problem. The model was built and then validated on three large non-overlapping intensive care databases, containing data collected from adult patients in the U.S.A and the U.K. Our agent extracted implicit knowledge from an amount of patient data that exceeds many-fold the life-time experience of human clinicians and learned optimal treatment by having analysed myriads of (mostly sub-optimal) treatment decisions. We used state-of-the-art evaluation techniques (called high confidence off-policy evaluation) and demonstrated that the value of the treatment strategy of the AI agent was on average reliably higher than the human clinicians. In two large validation cohorts independent from the training data, mortality was the lowest in patients where clinicians’ actual doses matched the AI policy. We also gained insight into the model representations and confirmed that the AI agent relied on clinically and biologically meaningful parameters when making its suggestions. We conducted extensive testing and exploration of the behaviour of the AI agent down to the level of individual patient trajectories, identified potential sources of inappropriate behaviour and offered suggestions for future model refinements.
If validated, our model could provide individualized and clinically interpretable treatment decisions for sepsis that may improve patient outcomes.Open Acces
