RT Journal Article SR Electronic T1 An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes JF bioRxiv FD Cold Spring Harbor Laboratory SP 062307 DO 10.1101/062307 A1 Katherine Redfield Chang A1 Xinghua Lou A1 Theofanis Karaletsos A1 Christopher Crosbie A1 Stuart Gardos A1 David Artz A1 Gunnar Rätsch YR 2016 UL http://biorxiv.org/content/early/2016/07/06/062307.abstract AB 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.