Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning

Zexian Zeng; Liang Yao; Ankita Roy; Xiaoyu Li; Sasa Espino; Susan E. Clare; Seema A. Khan; Yuan Luo

doi:10.1007/s41666-019-00046-3

ScienceGate Book Chapters

JOURNAL ARTICLE

Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning

Zexian Zeng Liang Yao Ankita Roy Xiaoyu Li Sasa Espino Susan E. Clare Seema A. Khan Yuan Luo

Year: 2019 Journal: Journal of Healthcare Informatics Research Vol: 3 (3)Pages: 283-299 Publisher: Springer Science+Business Media

DOI: 10.1007/s41666-019-00046-3

Get Full-Text PDF Get Analytical Report

Keywords:

Generalizability theory Artificial intelligence Breast cancer Test set Generalization Machine learning Data set Artificial neural network Medicine Computer science Test (biology) Convolutional neural network Cancer Internal medicine Mathematics Statistics

Metrics

Cited By

1.03

FWCI (Field Weighted Citation Impact)

Refs

0.72

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Biomedical Text Mining and Ontologies

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning in Healthcare

Physical Sciences → Computer Science → Artificial Intelligence

Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning

Metrics

Citation History

Topics

Related Documents

A machine learning approach to identifying delirium from electronic health records

Structuring Breast Cancer Spanish Electronic Health Records Using Deep Learning

Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records

Identifying Breast Cancer Using Machine Learning Algorithms