Fig 1. Illustration of proposed framework: The data preparation phase extracts 42 variables (demographic profiles, vital signs, laboratory tests, mechanical ventilation status of the patients, and the comorbidity profiles) from 16,546 distinct sepsis patients admitted to Beth Israel Deaconess Medical Center from the MIMIC-III database.

In the data analysis phase, we use archetypal analysis to find distinct states in sepsis. We validate that each state corresponds to patient clusters that are statistically distinct from the distribution of the cohort as a whole, and the SOFA score, SIRS score, and mortality rate are calculated to characterize each sepsis state. In primary function analysis, selected features from archetypes are used to identify the primary functions (namely, nervous system, inflammation and infection, liver function, kidney function, coagulation, respiratory function, and, cardiovascular function) of each sepsis state. In etiological analysis, we find correlation between pre-existing comorbidity profiles (30 types) and sepsis states. Finally, in progression analysis, we use higher-order Markov chains to model the dynamics of pathological processes of sepsis. We then use archetypal analysis to identify distinct types of sepsis state transitions and use z-score analysis to find representative clinical markers of each state transition.