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

Clinical Information and Decision Making (1373) Electronic Health Records (1031) Machine Learning (1433) Intensive Care Unit (ICU) (109)

Published on in Vol 22, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16374, first published .
Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

Authors of this article:

Subendhu Rongali1 Author Orcid Image ;   Adam J Rose2 Author Orcid Image ;   David D McManus3 Author Orcid Image ;   Adarsha S Bajracharya3 Author Orcid Image ;   Alok Kapoor3, 4 Author Orcid Image ;   Edgard Granillo5 Author Orcid Image ;   Hong Yu1, 3, 6, 7 Author Orcid Image
Article Authors Cited by (13) Tweetations (3) Metrics

Journals

  1. Syed M, Syed S, Sexton K, Syeda H, Garza M, Zozus M, Syed F, Begum S, Syed A, Sanford J, Prior F. Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review. Informatics 2021;8(1):16 View
  2. Oliver L, Hawco C, Viviano J, Voineskos A. From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation. Biological Psychiatry 2022;91(8):699 View
  3. Iwase S, Nakada T, Shimada T, Oami T, Shimazui T, Takahashi N, Yamabe J, Yamao Y, Kawakami E. Prediction algorithm for ICU mortality and length of stay using machine learning. Scientific Reports 2022;12(1) View
  4. Hayes C, Cucciare M, Martin B, Hudson T, Bush K, Lo-Ciganic W, Yu H, Charron E, Gordon A. Using Data Science to Improve Outcomes for Persons with Opioid use Disorder. Substance Abuse 2022;43(1):956 View
  5. Xie F, Yuan H, Ning Y, Ong M, Feng M, Hsu W, Chakraborty B, Liu N. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. Journal of Biomedical Informatics 2022;126:103980 View
  6. 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
  7. Goretti F, Oronti B, Milli M, Iadanza E. Deep Learning for Predicting Congestive Heart Failure. Electronics 2022;11(23):3996 View
  8. Herrero-Zazo M, Fitzgerald T, Taylor V, Street H, Chaudhry A, Bradley J, Birney E, Keevil V. Using Machine Learning to Model Older Adult Inpatient Trajectories From Electronic Health Records Data. SSRN Electronic Journal 2022 View
  9. Bhadouria A, Singh R. Machine learning model for healthcare investments predicting the length of stay in a hospital & mortality rate. Multimedia Tools and Applications 2023 View
  10. Yang Z, Mitra A, Liu W, Berlowitz D, Yu H. TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records. Nature Communications 2023;14(1) View
  11. Chae S, Street W, Ramaraju N, Gilbertson-White S. Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network. JCO Clinical Cancer Informatics 2024;(8) View
  12. Lazebnik T, Simon-keren L. Knowledge-integrated autoencoder model. Expert Systems with Applications 2024;252:124108 View