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20162021
most citedUnsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records

6 citations · 16 across the 8 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

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…

cs.IR2019

How Good is Artificial Intelligence at Automatically Answering Consumer Questions Related to Alzheimer's Disease?

Krishna B. Soundararajan, Sunyang Fu, Luke A. Carlson +4

Alzheimer's Disease (AD) is the most common type of dementia, comprising 60-80% of cases. There were an estimated 5.8 million Americans living with Alzheimer's dementia in 2019, an…

cs.IR2019★ 2 cited

Cross-lingual Data Transformation and Combination for Text Classification

Jun Jiang, Shumao Pang, Xia Zhao +4

Text classification is a fundamental task for text data mining. In order to train a generalizable model, a large volume of text must be collected. To address data insufficiency, cr…

stat.AP2019★ 6 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…

cs.IR2019

CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model

Sijia Liu, Yanshan Wang, Andrew Wen +6

Background: Widespread adoption of electronic health records (EHRs) has enabled secondary use of EHR data for clinical research and healthcare delivery. Natural language processing…