2018/08/23 by Charles C. Onu, Lara J. Kanbar, Onu, Charles C. +12
Engineering · Mathematics · Medicine · Neuroscience · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (stat.ML) #Neonatal Respiratory Health Research #Neuroscience of respiration and sleep #Respiratory Support and Mechanisms #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1808.07989
Published in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
openalex publication_date 2018/08/23 · arxiv created 2018/08/24 · arxiv updated 2018/08/27 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
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.