| Keywords
|
Machine Learning, Electronic health records, Sample bias +
|
| Level
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Master +
|
| OneLineSummary
|
To evaluate the impact of sample bias on the predictive value of machine learning models built using EHR data +
|
| Prerequisites
|
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. +
|
| References
|
1. Verheij, Robert A., et al. "Possible So … 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.tabase." Scientific data 3 (2016): 160035.
|
| StudentProjectStatus
|
Open +
|
| Supervisors
|
Awais Ashfaq +
, Sławomir Nowaczyk +
|
| TimeFrame
|
Spring 2019 +
|
| Title
|
Biases in electronic health records +
|
| Categories |
StudentProject +
|
| Modification dateThis property is a special property in this wiki.
|
11 October 2018 04:53:18 +
|
