Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41576, first published .
Using the H2O Automatic Machine Learning Algorithms to Identify Predictors of Web-Based Medical Record Nonuse Among Patients in a Data-Rich Environment: Mixed Methods Study

Using the H2O Automatic Machine Learning Algorithms to Identify Predictors of Web-Based Medical Record Nonuse Among Patients in a Data-Rich Environment: Mixed Methods Study

Using the H2O Automatic Machine Learning Algorithms to Identify Predictors of Web-Based Medical Record Nonuse Among Patients in a Data-Rich Environment: Mixed Methods Study

Journals

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  2. Li P, Tang Y, Zeng Q, Mo C, Ali N, Bai B, Ji S, Zhang Y, Luo J, Liang H, Wu R. Diagnostic performance of machine learning in systemic infection following percutaneous nephrolithotomy and identification of associated risk factors. Heliyon 2024;10(10):e30956 View
  3. Liu L, Zhang R, Shi Y, Sun J, Xu X. Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis. Scientific Reports 2024;14(1) View
  4. Pantic I, Paunovic Pantic J. Artificial Intelligence in Chromatin Analysis: A Random Forest Model Enhanced by Fractal and Wavelet Features. Fractal and Fractional 2024;8(8):490 View