Journal article
SCOSY: A Biomedical Collaboration Recommendation System
Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2018, pp 3987-3990
Jul 2018
PMID: 30441232
Abstract
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.
Metrics
Details
- Title
- SCOSY: A Biomedical Collaboration Recommendation System
- Creators
- Jorge Guerra - Children's Hospital of PhiladelphiaWei Quan - Children's Hospital of PhiladelphiaKai Li - Drexel UniversityLuis Ahumada - Johns Hopkins All Children's Hospital;Flaura Winston - Children's Hospital of PhiladelphiaBimal Desai - Children's Hospital of PhiladelphiaJanine Guerra - College of Arts and Sciences (1990-)
- Publication Details
- Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2018, pp 3987-3990
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Arts and Sciences
- Web of Science ID
- WOS:000596231904111
- Scopus ID
- 2-s2.0-85056651619
- Other Identifier
- 9781538636466; 1538636468; 991019173431204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Biomedical
- Engineering, Electrical & Electronic