Lung cancer is a leading cause of death in developed countries. The paper presents a text mining system using Support Vector Machines for detecting lung cancer admissions. Performance of the system using different clinical data sources is evaluated. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient demographics. Results show that linking data sources significantly improves classification performance with a maximum F-Score improvement of 0.057.
|Title of host publication||Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015)|
|Editors||Katrina Barbuto, Louise Schaper, Karin Verspoor|
|Place of Publication||Aachen Germany|
|Pages||1 - 7|
|Number of pages||7|
|Publication status||Published - 2015|
|Event||Big Data in Health Analytics 2015 - Swissotel Sydney, Sydney, Australia|
Duration: 20 Oct 2015 → 21 Oct 2015
http://ceur-ws.org/Vol-1468/ (CEUR Workshop Proceedings)
|Conference||Big Data in Health Analytics 2015|
|Abbreviated title||BigData 2015|
|Period||20/10/15 → 21/10/15|
|Other||We are pleased to invite you to respond to our Call for Submissions for Big Data 2015, Australasia’s Big Data In Biomedicine and Healthcare Conference, to be held from 20-21 October 2015 at the Swissotel Sydney.|
With a strong focus on collaboration, this year’s theme – Big data analytics: Leveraging capability in healthcare – reflects the opportunity for healthcare practitioners, information specialists in healthcare environments, university and research institute scientists to share their knowledge.