Published on in Vol 2 (2023)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/40755, first published
.
![Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework](https://asset.jmir.pub/assets/925d618eb488c0ad68d206c8f926bc2a.png 480w,https://asset.jmir.pub/assets/925d618eb488c0ad68d206c8f926bc2a.png 960w,https://asset.jmir.pub/assets/925d618eb488c0ad68d206c8f926bc2a.png 1920w,https://asset.jmir.pub/assets/925d618eb488c0ad68d206c8f926bc2a.png 2500w)
Journals
- Steiger E, Kroll L. Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework. JMIR AI 2023;2:e40755 View
- El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View
- Tansitpong P. Probabilistic Model of Patient Classification Using Bayesian Model. International Journal of Reliable and Quality E-Healthcare 2024;13(1):1 View