title: Feasibility of using a large Clinical Data Warehouse to automate the selection of diagnostic cohorts.
abstract: Data from Clinical Data Warehouses (CDWs) can be used for retrospective studies and for benchmarking. However, automated identification of cases from large datasets containing data items in free text fields is challenging. We developed an algorithm for categorizing pediatric patients presenting with respiratory distress into Bronchiolitis, Bacterial pneumonia and Asthma using clinical variables from a CDW. A feasibility study of this approach indicates that case selection may be automated.
authors.affiliation: Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Reejis
authors.initials: R
authors.lastname: Stephen
authors.forename: Aziz
authors.initials: A
authors.lastname: Boxwala
authors.forename: Paul
authors.initials: P
authors.lastname: Gertman
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000175:diagnosis; D035843:Biomedical Research; D001988:Bronchiolitis / Q000175:diagnosis; D016022:Case-Control Studies; D002648:Child; D003937:Diagnosis, Differential; D005240:Feasibility Studies; D006801:Humans; D018579:Patient Selection; D018410:Pneumonia, Bacterial / Q000175:diagnosis; D012120:Respiration Disorders / Q000209:etiology