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IET Cyber-Systems and Robotics
IET Cyber-Systems and Robotics  2021, Vol. 3 Issue (3): 228-244    DOI: 10.1049/csy2.12031
    
A study on preterm birth predictions using physiological signals, medical health record information and low-dimensional embedding methods
A study on preterm birth predictions using physiological signals, medical health record information and low-dimensional embedding methods
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摘要： Preterm births have been seen to have psychological and financial implications; current surveys suggest that amongst the various methods of preterm prediction, there is yet to exist a reliable and standard means of predicting preterm births. This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester, alongside information from the patients' medical health record regarding the pregnancy, towards preterm prediction and an associated delivery imminency timeline. In addition to this, the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored. The classification exercises were carried out using a support vector machine and decision tree, both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.
Abstract: Preterm births have been seen to have psychological and financial implications; current surveys suggest that amongst the various methods of preterm prediction, there is yet to exist a reliable and standard means of predicting preterm births. This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester, alongside information from the patients' medical health record regarding the pregnancy, towards preterm prediction and an associated delivery imminency timeline. In addition to this, the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored. The classification exercises were carried out using a support vector machine and decision tree, both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.
出版日期: 2022-09-06
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Ejay Nsugbe
Oluwarotimi William Samuel
Ibrahim Sanusi
Mojisola Grace Asogbon
Guanglin Li

引用本文:

Ejay Nsugbe, Oluwarotimi William Samuel, Ibrahim Sanusi, Mojisola Grace Asogbon, Guanglin Li. A study on preterm birth predictions using physiological signals, medical health record information and low-dimensional embedding methods. IET Cyber-Systems and Robotics, 2021, 3(3): 228-244.

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https://www.zjujournals.com/iet-csr/CN/10.1049/csy2.12031    或    https://www.zjujournals.com/iet-csr/CN/Y2021/V3/I3/228

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