An empirical analysis of topic modeling for mining cancer clinical notes Conference Paper
| Authors: | Chan, K. R.; Lou, X.; Karaletsos, T.; Crosbie, C.; Gardos, S.; Artz, D.; Ratsch, G. |
| Title: | An empirical analysis of topic modeling for mining cancer clinical notes |
| Conference Title: | 2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 |
| Abstract: | Using a variety of techniques including Topic Modeling, Principal Component Analysis and Bi-clustering, we explore electronic patient records in the form of unstructured clinical notes and genetic mutation test results. Our ultimate goal is to gain insight into a unique body of clinical data, specifically regarding the topics discussed within the note content and relationships between patient clinical notes and their underlying genetics. © 2013 IEEE. |
| Keywords: | principal component analysis; clinical notes; electronic medical records; genetic mutations; topic modeling |
| Journal Title | Proceedings of the IEEE 13th International Conference on Data Mining Workshops |
| Conference Dates: | 2013 Dec 7-10 |
| Conference Location: | Dallas, TX |
| ISBN: | 978-1-4799-3143-9 |
| Publisher: | IEEE |
| Location: | Dallas, TX |
| Date Published: | 2013-01-01 |
| Start Page: | 56 |
| End Page: | 63 |
| Language: | English |
| DOI: | 10.1109/icdmw.2013.91 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013 -- Proc. - IEEE Int. Conf. Data Min. Workshops, ICDMW -- Conference code: 104247 -- Export Date: 1 May 2014 -- Art. No.: 6753903 -- Sponsors: -- 7 December 2013 through 10 December 2013 -- Source: Scopus |
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