title: Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.
abstract: Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance.
  
  
  
  Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-sampling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection; analyse the learning curve; examine the effect of restricting admissions to only those containing reports from all data sources; and examine the impact of reducing the sub-sampling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness.
  
  
  
  Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports.
  
  
  
  Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios; the choice of the most effective combination of data sources depends on the specific disease to be classified.
authors.affiliation: Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia; School of Science, RMIT University, Melbourne, Australia; Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia. Electronic address: skocbek@gmail.com.
authors.forename: Simon
authors.initials: S
authors.lastname: Kocbek
authors.affiliation: School of Science, RMIT University, Melbourne, Australia.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Cavedon
authors.affiliation: Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Martinez
authors.affiliation: Mercy Health, Heidelberg, Australia; Faculty of Information Technology, Monash University, Clayton, Australia.
authors.forename: Christopher
authors.initials: C
authors.lastname: Bain
authors.affiliation: Health Informatics Department, Alfred Hospital, Melbourne, Australia; Now with OzeScribe, Melbourne, Australia.
authors.forename: Chris Mac
authors.initials: CM
authors.lastname: Manus
authors.affiliation: Faculty of Information Technology, Monash University, Clayton, Australia.
authors.forename: Gholamreza
authors.initials: G
authors.lastname: Haffari
authors.affiliation: Faculty of Information Technology, Monash University, Clayton, Australia.
authors.forename: Ingrid
authors.initials: I
authors.lastname: Zukerman
authors.affiliation: Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia.
authors.forename: Karin
authors.initials: K
authors.lastname: Verspoor
mesh_terms: D057225:Data Mining; D004194:Disease / Q000145:classification*; D006753:Hospital Records; D006760:Hospitalization; D006801:Humans; D009323:Natural Language Processing; D010349:Patient Compliance; D060388:Support Vector Machine
keywords: Cancer record retrieval; Electronic Health Records; Natural Language Processing; Pathology; Radiology; Text mining