]> 2026-04-04T11:08:08+02:00 Biases in electronic health records 0 en Master To evaluate the impact of sample bias on the predictive value of machine learning models built using EHR data Good knowledge of applied mathematics. An ability to implement state-of-the-art algorithms in a suitable programming environment. An interest in machine learning algorithms and medical data analysis. 1. Verheij, Robert A., et al. "Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse." Journal of medical Internet research 20.5 (2018). 2. Gianfrancesco, Milena A., et al. "Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data." JAMA internal medicine (2018). 3. Johnson, Alistair EW, et al. "MIMIC-III, a freely accessible critical care database." Scientific data 3 (2016): 160035. Open Spring 2019 Biases in electronic health records 2018-10-11T04:53:18Z 2458402.7036806 Biases in electronic health records 0 [2,"smw-datavalue-property-restricted-annotation-use","Keywords"] Biases in electronic health records# ERRd9c33d5ac8a89308063e13c4b212fa7c