The Exchangeable Markov Multi-states Growth Process Incorporate with an Artificial Neural Network of Preterm Infants in an Incubator
Science Journal of Applied Mathematics and Statistics
Volume 7, Issue 4, August 2019, Pages: 56-62
Received: Aug. 6, 2019;
Accepted: Aug. 26, 2019;
Published: Oct. 9, 2019
Views 525 Downloads 87
Jean Pierre Namahoro, Department of Mathematics, Faculty of Statistics, China University of Geosciences (Wuhan), Wuhan, PR. China
Xiao Haijun, Department of Mathematics, Faculty of Statistics, China University of Geosciences (Wuhan), Wuhan, PR. China
The standard incubator used to monitor the development of preterm infants, with much attention for random optimization can interrupt the three main parameters (oxygen, environmental temperature, and humidity) responsible for preterm growth. The artificial neural network (ANN) has been recently proposed as a novel technique to control those parameters to provide a better and stabilized environment in an incubator. Unfortunately, this novel technique cannot continuously provide and indicate the update challenge of preterm growth. The objective of this paper is to apply a Markov multi-state growth process incorporates with multilayer feed-forward artificial neural network as an improved methodology to continuously control and provide an update of preterm growth in an incubator. The exchangeable Markov growth process, transition graph, and artificial neural network discussed on and applied in the designed incubator as methodology in paper and then make a joint density function of Markov multi-states growth process through multi-steps designed Algorithm to get the theoretical results. The updated measurements (weight, height, and head-perimeter) associated with controlled parameters used as input to the threshold logic unit (TLU) of ANN and then distinguish whether the growth process is abnormal or normal at each state. The summarized algorithm and multilayer feed-forward ANN utilized the panel data collected at Murunda hospital in Rwanda as input to show the application of improved methodology proposed in this paper, specifically, multi-state growth process of preterm infants across gender. As results, the continuous exchangeability of the growth process at each state has updated and may show abnormal or normal of growth process, and then sensors may notify these change through the joint density function of Markov multi-states growth process. Thus, improved methodology can increase the security and minimize time consumption in continuous monitoring growth process in an advanced way in time this idea has been implemented.
Jean Pierre Namahoro,
The Exchangeable Markov Multi-states Growth Process Incorporate with an Artificial Neural Network of Preterm Infants in an Incubator, Science Journal of Applied Mathematics and Statistics.
Vol. 7, No. 4,
2019, pp. 56-62.
Goldenberg, R. L., et al., The preterm birth syndrome: issues to consider in creating a classification system. American journal of obstetrics and gynecology, 2012. 206 (2): p. 113-118.
Silverman, W. A., F. J. Agate, and J. W. Fertig, A sequential trial of the nonthermal effect of atmospheric humidity on survival of newborn infants of low birth weight. Pediatrics, 1963. 31 (5): p. 719-724.
Ramsay, S. M. and R. M. Santella, The definition of life: a survey of obstetricians and neonatologists in New York City hospitals regarding extremely premature births. Maternal and child health journal, 2011. 15 (4): p. 446-452.
Hey, E., The relation between environmental temperature and oxygen consumption in the new-born baby. The Journal of Physiology, 1969. 200 (3): p. 589-603.
Mondlane, R., A. De Graca, and G. Ebrahim, Skin-to-skin contact as a method of body warmth for infants of low birth weight. Journal of tropical pediatrics, 1989. 35 (6): p. 321-326.
Mazahery, H., et al., Air displacement plethysmography (pea pod) in full-term and pre-term infants: a comprehensive review of accuracy, reproducibility, and practical challenges. Maternal health, neonatology, and perinatology, 2018. 4 (1): p. 12.
Ramel, S. E., et al., Greater early gains in fat-free mass, but not fat mass, are associated with improved neurodevelopment at 1 year corrected age for prematurity in very low birth weight preterm infants. The Journal of pediatrics, 2016. 173: p. 108-115.
Demerath, E. W. and D. A. Fields, Body composition assessment in the infant. American Journal of Human Biology, 2014. 26 (3): p. 291-304.
Amer, G. M. and K. M. Al-Aubidy. Novel Technique to Control the Premature Infant Incubator System Using ANN. in Proceedings of Third International Conference on Systems, Signals & Devices (SSD 2005), Sousse, Tunisia. 2005.
Aalen, O., O. Borgan, and H. Gjessing, Survival and event history analysis: a process point of view. 2008: Springer Science & Business Media.
Mantyla, V. M., Discrete hidden Markov models with application to isolated user-dependent hand gesture recognition. VTT publications, 2001. 4 (4): p. 9.
Saeedi, A. and A. Bouchard-Côté. Priors over recurrent continuous-time processes. in Advances in Neural Information Processing Systems. 2011.
Hajiaghayi, M., et al. Efficient continuous-time Markov chain estimation. in International Conference on Machine Learning. 2014.
Rao, V., and Y. W. Teh. MCMC for continuous-time discrete-state systems. in Advances in Neural Information Processing Systems. 2012.
Council, N. R., The prevention and treatment of missing data in clinical trials. 2010: National Academies Press.
Jepsen, P., H. Vilstrup, and P. K. Andersen, The clinical course of cirrhosis: the importance of multistate models and competing risks analysis. Hepatology, 2015. 62 (1): p. 292-302.
Dempsey, W., Exchangeable, Markov multi-state survival process. arXiv preprint arXiv: 1810.10598, 2018.
Lagona, F. and E. Barbi, Segmentation of mortality surfaces by hidden Markov models. Statistical Modelling, 2018: p. 1471082X18777806.
Shiruru, K., AN INTRODUCTION TO ARTIFICIAL NEURAL NETWORK. Vol. 1. 2016. 27-30.
Yao, X., Evolving artificial neural networks. Proceedings of the IEEE, 1999. 87 (9): p. 1423-1447.
García-Pedrajas, N., C. Hervás-Martínez, and J. Muñoz-Pérez, COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on neural networks, 2003. 14 (3): p. 575-596.