title: Data mining issues for improved birth outcomes.
abstract: Issues obstructing progress in data mining for improved health outcomes include data quality problems, data redundancy, data inconsistency, repeated measures, temporal (time-contextual) measures, and data volume. Related issues involve theoretical and technical problems involving uncertainty management, missing data and missing values, and matching appropriate data mining techniques to patient data sets. Results of data mining research in progress are reported for Duke University's perinatal database that contains nearly a decade of clinical patient data, 71,753 database (patient) records and 4-5000 variables per patient.
authors.affiliation: Duke University Durham, NC 27710, USA. linda.goodwin@duke.edu
authors.forename: L
authors.initials: L
authors.lastname: Goodwin
authors.forename: J
authors.initials: J
authors.lastname: Prather
authors.forename: K
authors.initials: K
authors.lastname: Schlitz
authors.forename: M A
authors.initials: MA
authors.lastname: Iannacchione
authors.forename: M
authors.initials: M
authors.lastname: Hage
authors.forename: W E
authors.initials: WE
authors.lastname: Hammond
authors.forename: J
authors.initials: J
authors.lastname: Grzymala-Busse
mesh_terms: D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D006801:Humans; D010363:Pattern Recognition, Automated; D011247:Pregnancy; D011256:Pregnancy Outcome