Work

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records

Year: 2019

Type: article

Source: Journal of Biomedical Informatics

DOI: 10.1016/j.jbi.2019.103364

Authors: Yanshan Wang, Yiqing Zhao, Terry M. Therneau, Elizabeth J. Atkinson, Ahmad P. Tafti, Nan Zhang, Shreyasee Amin, Andrew H. Limper, Sundeep Khosla, Hongfang Liu

Institution: Mayo Clinic

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: bronze

Funders: Mayo Clinic, National Institutes of Health, U.S. Public Health Service, National Institute on Aging, National Institute of General Medical Sciences, U.S. National Library of Medicine, National Center for Advancing Translational Sciences

Awards: Multimorbidity and Aging: Rochester Epidemiology Project, Natural language processing for clinical and translational research, Open Health Natural Language Processing Collaboratory, Physiology of Bone Metabolism in an Aging Population, G824560934, Mayo Clinic Center for Clinical and Translational Science (CCaTS), Semi-structured Information Retrieval in Clinical Text for Cohort Identification

OpenAlex ID: W2996824246

FWCI: 4.729

Cites: 42

Cited by: 168

Related to: 10