  After birth, extremely preterm infants often require specialized respiratory
management in the form of invasive mechanical ventilation (IMV). Protracted IMV
is associated with detrimental outcomes and morbidities. Premature extubation,
on the other hand, would necessitate reintubation which is risky, technically
challenging and could further lead to lung injury or disease. We present an
approach to modeling respiratory patterns of infants who succeeded extubation
and those who required reintubation which relies on Markov models. We compare
the use of traditional Markov chains to semi-Markov models which emphasize
cross-pattern transitions and timing information, and to multi-chain Markov
models which can concisely represent non-stationarity in respiratory behavior
over time. The models we developed expose specific, unique similarities as well
as vital differences between the two populations.
