An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes

Author

Chan, Katherine Redfield ; Xinghua Lou ; Karaletsos, Theofanis ; Crosbie, Christopher ; Gardos, Stuart ; Artz, David ; Ratsch, Gunnar

Author_Institution

Comput. Biol. Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA

fYear

2013

fDate

7-10 Dec. 2013

Firstpage

56

Lastpage

63

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.

Keywords

cancer; data mining; electronic health records; pattern clustering; principal component analysis; cancer clinical notes mining; clinical data; electronic patient records; genetic mutation test results; principal component analysis; topic modeling empirical analysis; unstructured clinical notes; Cancer; Correlation; Genetic mutations; History; Lungs; Principal component analysis; clinical notes; electronic medical records; genetic mutations; principal component analysis; topic modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on

Conference_Location

Dallas, TX

Print_ISBN

978-1-4799-3143-9

Type

conf

DOI

10.1109/ICDMW.2013.91

Filename

6753903