| Predicting high-risk preterm birth using artificial neural networks. | |
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MedLine Citation:
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PMID: 16871723 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model. |
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Authors:
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Christina Catley; Monique Frize; C Robin Walker; Dorina C Petriu |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society Volume: 10 ISSN: 1089-7771 ISO Abbreviation: IEEE Trans Inf Technol Biomed Publication Date: 2006 Jul |
Date Detail:
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Created Date: 2006-07-28 Completed Date: 2006-09-05 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 9712259 Medline TA: IEEE Trans Inf Technol Biomed Country: United States |
Other Details:
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Languages: eng Pagination: 540-9 Citation Subset: IM |
Affiliation:
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Systems and Computer Engineering Department, Carleton University, Ottawa, ON, Canada. ccatley@ieee.org |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Artificial Intelligence Canada / epidemiology Decision Support Systems, Clinical* Diagnosis, Computer-Assisted / methods* Humans Incidence Infant, Newborn Nerve Net* Outcome Assessment (Health Care) / methods* Pattern Recognition, Automated / methods Perinatal Care / methods Premature Birth / diagnosis*, epidemiology* Reproducibility of Results Risk Assessment / methods* Risk Factors Sensitivity and Specificity |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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