TY - JOUR
T1 - Text mining electronic hospital records to automatically classify admissions against disease
T2 - Measuring the impact of linking data sources
AU - Kocbek, Simon
AU - Cavedon, Lawrence
AU - Martinez, David
AU - Bain, Christopher
AU - Mac Manus, Chris
AU - Haffari, Gholamreza
AU - Zukerman, Ingrid
AU - Verspoor, Karin
PY - 2016/10/11
Y1 - 2016/10/11
N2 - 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.
AB - 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.
KW - Cancer record retrieval
KW - Text mining
KW - Natural Language Processing
KW - Electronic Health Records
KW - Radiology
KW - Pathology
UR - http://www.scopus.com/inward/record.url?scp=84991794149&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2016.10.008
DO - 10.1016/j.jbi.2016.10.008
M3 - Article
VL - 64
SP - 158
EP - 167
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
SN - 1532-0464
ER -