This paper is in the following e-collection/theme issue:

Machine Learning (1465) Artificial Intelligence (1371) Electronic Health Records (1053) Stroke (163)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19905, first published .
Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study

Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study

Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study

Authors of this article:

Yanqun Huang1, 2 Author Orcid Image ;   Ni Wang1, 2 Author Orcid Image ;   Zhiqiang Zhang1, 2 Author Orcid Image ;   Honglei Liu1, 2 Author Orcid Image ;   Xiaolu Fei3 Author Orcid Image ;   Lan Wei3 Author Orcid Image ;   Hui Chen1, 2 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Huang Y, Zheng Z, Ma M, Xin X, Liu H, Fei X, Wei L, Chen H. Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study. Journal of Medical Internet Research 2022;24(8):e37486 View
  2. Liang Y, Guo C. Heart failure disease prediction and stratification with temporal electronic health records data using patient representation. Biocybernetics and Biomedical Engineering 2023;43(1):124 View
  3. Huang Y, Wang M, Zheng Z, Ma M, Fei X, Wei L, Chen H. Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients. Journal of Biomedical Informatics 2023;143:104427 View
  4. Guo B, Liu H, Niu L. Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records. Frontiers in Neuroscience 2023;17 View