  The disease trajectory for clinical sepsis, in terms of temporal cytokine and
phenotypic dynamics, can be interpreted as a random dynamical system. The
ability to make accurate predictions about patient state from clinical
measurements has eluded the biomedical community, primarily due to the paucity
of relevant and high-resolution data. We have utilized two distinct neural
network architectures, Long Short-Term Memory and Multi-Layer Perceptron, to
take a time sequence of five measurements of eleven simulated serum cytokine
concentrations as input and to return both the future cytokine trajectories as
well as an aggregate metric representing the patient's state of health. The
neural networks converged within 50 epochs for cytokine trajectory predictions
and health-metric regressions, with the expected amount of error (due to
stochasticity in the simulation). The mapping from a specific cytokine profile
to a state-of-health is not unique, and increased levels of inflammation result
in less accurate predictions. Due to the propagation of machine learning error
combined with computational model stochasticity over time, the network should
be re-grounded in reality daily as predictions can diverge from the true model
trajectory as the system evolves towards a probabilistic basin of attraction.
This work serves as a proof-of-concept for the use of artificial neural
networks to predict disease progression in sepsis. This work is not intended to
replace a trained clinician, rather the goal is to augment intuition with
quantifiable statistical information to help them make the best decisions. We
note that this relies on a valid computational model of the system in question
as there does not exist sufficient data to inform a machine-learning trained,
artificially intelligent, controller.
