Building knowledge in a complex preterm birth problem domain.

L Goodwin; S Maher; L Ohno-Machado; M A Iannacchione; P Crockett; S Dreiseitl; S Vinterbo; W Hammond

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PubMed · 11079894

Building knowledge in a complex preterm birth problem domain.

L Goodwin·S Maher·L Ohno-Machado·M A Iannacchione·P Crockett·S Dreiseitl·S Vinterbo·W Hammond

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Abstract

Data mining methods used a racially diverse sample (n = 19,970) of pregnant women and 1,622 variables that were collected in Duke's TMR electronic patient record over a 10-year period. Different statistical and data mining methods were similar when compared using receiver operating characteristic (ROC) curves. Best results found that seven demographic variables yielded .72 and addition of hundreds of other clinical variables added only .03 to the area under the curve (AUC). Similar results across methods suggest that results were data-driven and not method-dependent, and that demographic variables may offer a small set of parsimonious variables with predictive accuracy in a racially diverse population. Work to determine relevant variables for improved predictive accuracy is ongoing.

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Area Under Curve Artificial Intelligence Decision Support Techniques Female Humans Infant, Newborn Infant, Premature Information Storage and Retrieval Logistic Models Neural Networks, Computer Obstetric Labor, Premature Pregnancy ROC Curve Risk Assessment Statistics as Topic Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.

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L Goodwin, S Maher, L Ohno-Machado, M A Iannacchione, P Crockett, S Dreiseitl, S Vinterbo, W Hammond. 2000. Building knowledge in a complex preterm birth problem domain.. https://pubmed.ncbi.nlm.nih.gov/11079894/

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