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Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing
[article]
2018
arXiv
pre-print
Preserved Fulltext
Their maximum likelihood estimates are given by [13] : π j = # of sequences starting in j # of sequences ∀j ∈ S (1) A i,j = n ij j n ij ∀ i, j ∈ S (2) where n ij is the number of time steps in which a ...
transition from state i to j occurred. ...
arXiv:1808.07991v1
fatcat:zwzpjbdqq5cedcwivgzlc6aumi
A Semi-Markov Chain Approach to Modeling Respiratory Patterns Prior to Extubation in Preterm Infants
[article]
2018
arXiv
pre-print
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J. Kanbar and R. E. Kearney are with the department of Biomedical Engineering, McGill University, Montreal, QC H3A 2B4, Canada (e-mail: lara.kanbar@mail.mcgill.ca; robert.kearney@ mcgill.ca) 3 W. ...
Empirically, the transition probabilities can be computed using maximum likelihood estimation: T i,j = n ij j n ij ∀ i, j ∈ S (2) where n ij is number of times the transition from state i to state j was ...
arXiv:1808.07989v1
fatcat:jtgcqjlg5bh2dlydpdezrhua6m
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
[article]
2018
arXiv
pre-print
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Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready. Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates. We present an approach using Random Forest classifiers for the analysis of
arXiv:1808.07992v1
fatcat:sgcka7ywfndedcv5brybvheqe4
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... cardiorespiratory variability to predict extubation readiness. We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag. By incorporating clinical domain knowledge, we further demonstrate that our classifier could have identified 71% of infants who failed extubation, while maintaining a success detection rate of 78%.
Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory behavior: study protocol
2017
BMC Pediatrics
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Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with
doi:10.1186/s12887-017-0911-z
pmid:28716018
pmcid:PMC5512825
fatcat:nkozwhdtyzccjgyeir6hiyr5rm
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... ed morbidities. A variety of objective measures have been proposed to better define the optimal time for extubation, but none have proven clinically useful. In a pilot study, investigators from this group have shown promising results from sophisticated, automated analyses of cardiorespiratory signals as a predictor of extubation readiness. The aim of this study is to develop an automated predictor of extubation readiness using a combination of clinical tools along with novel and automated measures of cardiorespiratory behavior, to assist clinicians in determining when extremely preterm infants are ready for extubation. Methods: In this prospective, multicenter observational study, cardiorespiratory signals will be recorded from 250 eligible extremely preterm infants with birth weights ≤1250 g immediately prior to their first planned extubation. Automated signal analysis algorithms will compute a variety of metrics for each infant, and machine learning methods will then be used to find the optimal combination of these metrics together with clinical variables that provide the best overall prediction of extubation readiness. Using these results, investigators will develop an Automated system for Prediction of EXtubation (APEX) readiness that will integrate the software for data acquisition, signal analysis, and outcome prediction into a single application suitable for use by medical personnel in the neonatal intensive care unit. The performance of APEX will later be prospectively validated in 50 additional infants. Discussion: The results of this research will provide the quantitative evidence needed to assist clinicians in determining when to extubate a preterm infant with the highest probability of success, and could produce significant improvements in extubation outcomes in this population.
A Mixture of Experts Model for Extubation
2017
IJSTE-International Journal of Science Technology & Engineering |
unpublished
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Lara J. Kanbar, et.al [1] described the automated validation and quality control procedure without any user supervision and takes care of data acquired from different locations. ...
J. Kaczmarek, et.al [2] discussed a new predictor to help doctors to decide the suitable time to extubate. It uses modern machine learning approach. ...
fatcat:btr6ie544fhyhjth3lsup4qvmq
Microbial ecology of the South Atlantic Subtropical Gyre: a state-of-the-art review of an understudied ocean region
2021
Ocean and Coastal Research
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., LOZANO, J., STEPHENS, J., HARRIS, J. T., MIRARAB, S., XU, Z. Z., HAROON, M. F., KANBAR, J., ZHU,
C. & THOMAS, R. 2015. Both respiration and photosynthesis de- Q., SONG, S. ...
G., BREITHAUPT, P., WALTHER, K., KOPPE, R., BLECK, S., LARA, E., ARRIETA, J. M., GARCIA-ZARANDONA, I., BORAS, J. A.,
SOMMER, U. & JÜRGENS, K. 2008. ...
doi:10.1590/2675-2824069.20026lrf
fatcat:7pw2gejfjfdihljb6dqlkc4uk4
OBTENÇÃO DE BIODIESEL POR MEIO DA TRANSESTERIFICAÇÃO DO ÓLEO DE SOJA UTILIZANDO CATALISADOR DE KOH/AL2 O3 EM DIFERENTES COMPOSIÇÕES
[chapter]
2020
Ampliação e Aprofundamento de Conhecimentos nas Áreas das Engenharias
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In= Ln(A). 0,5
Eq. 6
Onde:
In: corrente nominal
Momento de inercia (J x ):
J x = J x1 + J x2
Eq.7
Onde:
Eq.7
Eq.8
Onde:
Eq.9
Torque nominal (Mn):
Eq.10
Torque de aceleração (M ...
BALDWIN, E.A.; HAGENMAIER, R; BAI, J (Ed. ...
doi:10.22533/at.ed.7442008049
fatcat:4ggvq4v2wbczdfj7eesjtkvxq4
Functional properties, neurochemistry and connectivity of medullary neural networks that control breathing and circulation
2022
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., 2009a; Kanbar et al., 2010; Marina et al., 2011) . ...
J. Comp. Neurol. 524:323-342, 2016. V C 2015 Wiley Periodicals, Inc. ...
doi:10.25949/19434287
fatcat:yd3vox5hhzhobc6hsoxxwbomeu