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

Clinical Information and Decision Making (1314) Electronic Health Records (961) Machine Learning (1370) Natural Language Processing (636) Ontologies, Classifications, and Coding (223)

Published on in Vol 23, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25113, first published .
Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study

Authors of this article:

Yen-Pin Chen1, 2, 3 Author Orcid Image ;   Yuan-Hsun Lo4 Author Orcid Image ;   Feipei Lai1 Author Orcid Image ;   Chien-Hua Huang3 Author Orcid Image
Article Authors Cited by (12) Tweetations (4) Metrics

Journals

  1. Sun Z, Lu X, Duan H, Li H. Deep Dynamic Patient Similarity Analysis: Model Development and Validation in ICU. Computer Methods and Programs in Biomedicine 2022;225:107033 View
  2. Chen Y, Huang C, Lo Y, Chen Y, Lai F. Combining attention with spectrum to handle missing values on time series data without imputation. Information Sciences 2022;609:1271 View
  3. Qiu Y, Lin F, Chen W, Xu M. Pre-training in Medical Data: A Survey. Machine Intelligence Research 2023;20(2):147 View
  4. Higaki A, Okayama H, Homma Y, Sano T, Kitai T, Yonetsu T, Torii S, Kohsaka S, Kuroda S, Node K, Matsue Y, Matsumoto S. Predictive value of neutrophil-to-lymphocyte ratio for the fatality of COVID-19 patients complicated with cardiovascular diseases and/or risk factors. Scientific Reports 2022;12(1) View
  5. Wang W, Ahn E, Feng D, Kim J. A Review of Predictive and Contrastive Self-supervised Learning for Medical Images. Machine Intelligence Research 2023;20(4):483 View
  6. Kokilepersaud K, Corona S, Prabhushankar M, AlRegib G, Wykoff C. Clinically Labeled Contrastive Learning for OCT Biomarker Classification. IEEE Journal of Biomedical and Health Informatics 2023;27(9):4397 View
  7. Chen Y, Ko P, Chi C, Chong K, Chen Y, Huang C. Association between independent practice time and patient outcomes in the emergency department: a retrospective study of residents in three urban hospitals in Taiwan. BMC Emergency Medicine 2023;23(1) View
  8. Romano M, Shih L, Paschalidis I, Au R, Kolachalama V. Large Language Models in Neurology Research and Future Practice. Neurology 2023;101(23):1058 View
  9. Hur K, Oh J, Kim J, Kim J, Lee M, Cho E, Moon S, Kim Y, Atallah L, Choi E. GenHPF: General Healthcare Predictive Framework for Multi-Task Multi-Source Learning. IEEE Journal of Biomedical and Health Informatics 2024;28(1):502 View
  10. Siebra C, Kurpicz-Briki M, Wac K. Transformers in health: a systematic review on architectures for longitudinal data analysis. Artificial Intelligence Review 2024;57(2) View
  11. Preiksaitis C, Ashenburg N, Bunney G, Chu A, Kabeer R, Riley F, Ribeira R, Rose C. The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR Medical Informatics 2024;12:e53787 View