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Tools, Programs and Algorithms (275)

Published on in Vol 4, No 4 (2016): Oct-Dec

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

Authors of this article:

Wael Farhan1 Author Orcid Image ;   Zhimu Wang1, 2 Author Orcid Image ;   Yingxiang Huang1 Author Orcid Image ;   Shuang Wang1 Author Orcid Image ;   Fei Wang3 Author Orcid Image ;   Xiaoqian Jiang1 Author Orcid Image
Article Authors Cited by (38) Tweetations (4) Metrics

Journals

  1. Khan A, Zubair S. Longitudinal Magnetic Resonance Imaging as a Potential Correlate in the Diagnosis of Alzheimer Disease: Exploratory Data Analysis. JMIR Biomedical Engineering 2020;5(1):e14389 View
  2. Tang F, Xiao C, Wang F, Zhou J. Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open 2018;1(1):87 View
  3. Huang Y, Lee J, Wang S, Sun J, Liu H, Jiang X. Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources. JMIR Medical Informatics 2018;6(2):e33 View
  4. Santiso S, Pérez A, Casillas A. Smoothing dense spaces for improved relation extraction between drugs and adverse reactions. International Journal of Medical Informatics 2019;128:39 View
  5. Pokharel S, Zuccon G, Li X, Utomo C, Li Y. Temporal tree representation for similarity computation between medical patients. Artificial Intelligence in Medicine 2020;108:101900 View
  6. Li Z, Roberts K, Jiang X, Long Q. Distributed learning from multiple EHR databases: Contextual embedding models for medical events. Journal of Biomedical Informatics 2019;92:103138 View
  7. Kim Y, El-Kareh R, Sun J, Yu H, Jiang X. Discriminative and Distinct Phenotyping by Constrained Tensor Factorization. Scientific Reports 2017;7(1) View
  8. Ashfaq A, Sant’Anna A, Lingman M, Nowaczyk S. Readmission prediction using deep learning on electronic health records. Journal of Biomedical Informatics 2019;97:103256 View
  9. Min X, Yu B, Wang F. Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD. Scientific Reports 2019;9(1) View
  10. Qureshi M, Qureshi K, Jeon G, Piccialli F. Deep learning-based ambient assisted living for self-management of cardiovascular conditions. Neural Computing and Applications 2022;34(13):10449 View
  11. Lee J, Hauskrecht M. Modeling multivariate clinical event time-series with recurrent temporal mechanisms. Artificial Intelligence in Medicine 2021;112:102021 View
  12. Lee T, Lee J, Jun C. Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling. Information Sciences 2021;568:403 View
  13. Getzen E, Ungar L, Mowery D, Jiang X, Long Q. Mining for equitable health: Assessing the impact of missing data in electronic health records. Journal of Biomedical Informatics 2023;139:104269 View
  14. B. V. V, Jayanthila Devi A. Design and Development of Efficient Model to Predict Neurodegenerative Disorders Using Advanced LSTM: A Review of Literature. International Journal of Case Studies in Business, IT, and Education 2022:521 View
  15. Yoo J, Lee J, Min J, Choi S, Kwon J, Cho I, Lim C, Choi M, Cha W. Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study. Journal of Medical Internet Research 2022;24(7):e37928 View
  16. Liu X, Wang H, He T, Liao Y, Jian C. Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review. Journal of Physics: Conference Series 2022;2188(1):012007 View
  17. Memarzadeh H, Ghadiri N, Samwald M, Lotfi Shahreza M. A study into patient similarity through representation learning from medical records. Knowledge and Information Systems 2022;64(12):3293 View
  18. Lee T, Kim S, Jun C, Lee J. Word2vec-Based Efficient Privacy-Preserving Shared Representation Learning for Federated Recommendation System in a Cross-Device Setting. SSRN Electronic Journal 2023 View
  19. Liang Z, Zhang Z, Chen H, Zhang Z. Disease prediction based on multi-type data fusion from Chinese electronic health record. Mathematical Biosciences and Engineering 2022;19(12):13732 View
  20. Yue Z, Yan T, Xu H, Liu Y, Hong Y, Chen G, Xie T, Tao L. A systematic review on the state-of-the-art strategies for protein representation. Computers in Biology and Medicine 2023;152:106440 View
  21. V S A, Bizotto B, Sathiyanarayanan M. Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach. Journal of Machine and Computing 2023:159 View
  22. ZEHRAOUI F, Sendi N, Abchiche-Mimouni N. MS-LSTMEA: Predicting Clinical Events for Hypertension Using Multi-Sources LSTM Explainable Approach. SSRN Electronic Journal 2022 View
  23. Li M, Lin Y, Chen H, Aparasu R. An unsupervised embedding harmonization system for privacy-preserving data mining in healthcare. IISE Transactions on Healthcare Systems Engineering 2024;14(1):1 View
  24. Lee T, Kim S, Lee J, Jun C. Word2Vec-based efficient privacy-preserving shared representation learning for federated recommendation system in a cross-device setting. Information Sciences 2023;651:119728 View
  25. Lee T, Kim S, Lee J, Jun C. HarmoSATE: Harmonized embedding-based self-attentive encoder to improve accuracy of privacy-preserving federated predictive analysis. Information Sciences 2024;662:120265 View
  26. Wang C, Yang X, Sun M, Gu Y, Niu J, Zhang W. Multimodal fusion network for ICU patient outcome prediction. Neural Networks 2024;180:106672 View
  27. Guo J, Kiryluk K, Wang S. PheW2P2V: a phenome-wide prediction framework with weighted patient representations using electronic health records. JAMIA Open 2024;7(3) View

Books/Policy Documents

  1. Jana S, Ray S, Adhikary P, Banerjee T. Cognitive Computing in Human Cognition. View
  2. Zhang T, Chen M, Bui A. Artificial Intelligence in Medicine. View
  3. Malygina T, Drokin I. Artificial Intelligence and Natural Language. View
  4. Bulgarelli L, Núñez-Reiz A, Deliberato R. Leveraging Data Science for Global Health. View
  5. Mansourvar M, Wiil U, Nøhr C. Emerging Technologies in Computing. View
  6. Getzen E, Ruan Y, Ungar L, Long Q. Statistics in Precision Health. View