TY - GEN
T1 - SCOSY
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
AU - Guerra, Jorge
AU - Quan, Wei
AU - Li, Kai
AU - Ahumada, Luis
AU - Winston, Flaura
AU - Desai, Bimal
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Finding relevant scientific articles and collaborators is a time-consuming and challenging task in today's information-rich environment. Despite this challenge, the study and development of recommendation systems, based on the authors' collaboration network, productivity and area of research, as topics of interest, have not been practically deployed in healthcare organizations. To address this known practice gap and to promote collaboration, Schosy was developed. This system collects publication metadata from PubMed, as the data source, and combining Collaborative and ContentBased Filtering techniques coupled with the Latent Dirichlet Allocation Topic Modeling algorithm, it recommends collaborators based on the authors' work, collaboration among the authors, Medical Subject Headings (MeSH) terms and the productivity of relevant researchers. As a result, this system provides an interpretable latent structure for collaborators and biomedical databases in order to enhance the experience of finding collaboration, for and by researchers and non-technical users.
AB - Finding relevant scientific articles and collaborators is a time-consuming and challenging task in today's information-rich environment. Despite this challenge, the study and development of recommendation systems, based on the authors' collaboration network, productivity and area of research, as topics of interest, have not been practically deployed in healthcare organizations. To address this known practice gap and to promote collaboration, Schosy was developed. This system collects publication metadata from PubMed, as the data source, and combining Collaborative and ContentBased Filtering techniques coupled with the Latent Dirichlet Allocation Topic Modeling algorithm, it recommends collaborators based on the authors' work, collaboration among the authors, Medical Subject Headings (MeSH) terms and the productivity of relevant researchers. As a result, this system provides an interpretable latent structure for collaborators and biomedical databases in order to enhance the experience of finding collaboration, for and by researchers and non-technical users.
UR - http://www.scopus.com/inward/record.url?scp=85056651619&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056651619&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513268
DO - 10.1109/EMBC.2018.8513268
M3 - Conference contribution
C2 - 30441232
AN - SCOPUS:85056651619
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3987
EP - 3990
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 July 2018 through 21 July 2018
ER -