Miller Children's and Women's Hospital and Long Beach Memorial Hospital 1 article published in JoVE Medicine Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications Dibakar Sigdel*1,2, Vincent Kyi*1,2, Aiden Zhang*1, Shaun P. Setty3, David A. Liem1,2,4, Yu Shi5, Xuan Wang5, Jiaming Shen5, Wei Wang1,6,7, JiaWei Han5, Peipei Ping1,2,4,6 1The NIH BD2K Center of Excellence in Biomedical Computing, University of California, Los Angeles, 2Department of Physiology, University of California, Los Angeles, 3Department of Pediatric and Adult Congenital Heart Surgery, Miller Children's and Women's Hospital and Long Beach Memorial Hospital, 4Department of Medicine/Cardiology, University of California, Los Angeles, 5NIH BD2K Program Centers of Excellence for Big Data Computing -- KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign (UIUC), 6Scalable Analytics Institute (ScAi), University of California, Los Angeles, 7Department of Computer Science, University of California, Los Angeles We present a protocol and associated programming code as well as metadata samples to support a cloud-based automated identification of phrases-category association representing unique concepts in user selected knowledge domain in biomedical literature. The phrase-category association quantified by this protocol can facilitate in depth analysis in the selected knowledge domain.
