most citedUnsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records

6 citations · 8 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2021

Unsupervised Learning to Subphenotype Delirium Patients from Electronic Health Records

Yiqing Zhao, Yuan Luo

Delirium is a common acute onset brain dysfunction in the emergency setting and is associated with higher mortality. It is difficult to detect and monitor since its presentations a…

cs.LG2021

Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping

David Oniani, Chen Wang, Yiqing Zhao +3

Cancer is responsible for millions of deaths worldwide every year. Although significant progress has been achieved in cancer medicine, many issues remain to be addressed for improv…

cs.LG20212 cited

Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features

David Oniani, Chen Wang, Yiqing Zhao +3

Cancer is responsible for millions of deaths worldwide every year. Although significant progress hasbeen achieved in cancer medicine, many issues remain to be addressed for improvi…

cs.IR2019

Clinical Concept Extraction: a Methodology Review

Sunyang Fu, David Chen, Huan He +10

Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinica…

stat.AP20196 cited

Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records

Yanshan Wang, Yiqing Zhao, Terry M. Therneau +6

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learnin…