Published on in Vol 6, No 1 (2021): Jan-Mar
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/23364, first published
.

Journals
- Jang B, Kim I. Machine-learning algorithms predict breast cancer patient survival from UK Biobank whole-exome sequencing data. Biomarkers in Medicine 2021;15(16):1529 View
- Fregoso-Aparicio L, Noguez J, Montesinos L, García-García J. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetology & Metabolic Syndrome 2021;13(1) View
- Clark S, Lomax N, Morris M, Pontin F, Birkin M. Clustering Accelerometer Activity Patterns from the UK Biobank Cohort. Sensors 2021;21(24):8220 View
- Mistry S, Riches N, Gouripeddi R, Facelli J. Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review. Artificial Intelligence in Medicine 2023;135:102461 View
- Atehortúa A, Gkontra P, Camacho M, Diaz O, Bulgheroni M, Simonetti V, Chadeau-Hyam M, Felix J, Sebert S, Lekadir K. Cardiometabolic risk estimation using exposome data and machine learning. International Journal of Medical Informatics 2023;179:105209 View
- Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?. DIGITAL HEALTH 2023;9 View
- Chellappan D, Rajaguru H. Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance. Diagnostics 2023;13(16):2654 View
- Site A, Nurmi J, Lohan E. Machine-Learning-Based Diabetes Prediction Using Multisensor Data. IEEE Sensors Journal 2023;23(22):28370 View
Books/Policy Documents
- Ibáñez-Redin G, Duarte O, Cagnani G, Oliveira O. Machine Learning for Advanced Functional Materials. View