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

Tools, Programs and Algorithms (275) Data Science (278) Articles* (13) Big Data (245)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38590, first published .
Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

Authors of this article:

Xiaojie Chen1 Author Orcid Image ;   Han Chen2 Author Orcid Image ;   Shan Nan1 Author Orcid Image ;   Xiangtian Kong3 Author Orcid Image ;   Huilong Duan1, 4 Author Orcid Image ;   Haiyan Zhu2, 5 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

Journals

  1. Li Q, Li J, Chen J, Zhao X, Zhuang J, Zhong G, Song Y, Lei L. A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records. BMC Cardiovascular Disorders 2024;24(1) View
  2. Muazu T, Mao Y, Muhammad A, Ibrahim M, Kumshe U, Samuel O. A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing. Computer Communications 2024;216:168 View
  3. Qiu T, Chen M, Gao S, Huang J, Wang W, Wang L, Li H. Application effect study of a combination of TeamSTEPPS with modularization teaching in the context of clinical instruction in trauma care. Scientific Reports 2024;14(1) View
  4. Yan J. A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients. PeerJ Computer Science 2024;10:e2017 View
  5. Cao S, Liu X, Song B, Hu Y. Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study. BMC Urology 2024;24(1) View
  6. Chi C, Moghadas-Dastjerdi H, Winkler A, Ao S, Chen Y, Wang L, Su P, Lin W, Tsai M, Huang C. Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records. Reviews in Cardiovascular Medicine 2023;24(9) View