Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
|ISBN||1532-0480 (Electronic) 1532-0464 (Linking)|
|Authors||Kocbek, S.; Cavedon, L.; Martinez, D.; Bain, C.; Manus, C. M.; Haffari, G.; Zukerman, I.; Verspoor, K.;|
|Publisher Name||JOURNAL OF BIOMEDICAL INFORMATICS|
|Published Date||2016-12-01 00:00:00|
|URL link to publisher's version||http://www.ncbi.nlm.nih.gov/pubmed/27742349|
|OpenAccess link to author's accepted manuscript version||https://publications.gimr.garvan.org.au/open-access/13997|