Simon Kocbek1, Lawrence Cavedon2, David Martinez3, Christopher Bain4, Chris Mac Manus5, Gholamreza Haffari6, Ingrid Zukerman6, Karin Verspoor3. 1. 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. 2. School of Science, RMIT University, Melbourne, Australia. 3. Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia. 4. Mercy Health, Heidelberg, Australia; Faculty of Information Technology, Monash University, Clayton, Australia. 5. Health Informatics Department, Alfred Hospital, Melbourne, Australia; Now with OzeScribe, Melbourne, Australia. 6. Faculty of Information Technology, Monash University, Clayton, Australia.
Abstract
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. Copyright Â
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. Copyright Â
Authors: Anthony Rios; Eric B Durbin; Isaac Hands; Susanne M Arnold; Darshil Shah; Stephen M Schwartz; Bernardo H L Goulart; Ramakanth Kavuluru Journal: J Biomed Inform Date: 2019-08-08 Impact factor: 6.317
Authors: Wei-Hung Weng; Kavishwar B Wagholikar; Alexa T McCray; Peter Szolovits; Henry C Chueh Journal: BMC Med Inform Decis Mak Date: 2017-12-01 Impact factor: 2.796