Towards Reproducible, Stable, and Robust Machine Learning Research in Clinical Environments is a dissertation that guides researchers to analyze their work through the lens of clinical deployment. It highlights the challenges a research project faces when considering clinical translation and the considerations needed to overcome them. It emphasizes the importance of healthcare data in medicine and showcases academic contributions and relevant publications. Finally, it summarizes the results of the work and provides future directions, thus providing a broad outlook.
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Towards Reproducible, Stable, and Robust Machine Learning Research in Clinical Environments is a dissertation that guides researchers to analyze their work through the lens of clinical deployment. It highlights the challenges a research project faces when considering clinical translation and the considerations needed to overcome them. It emphasizes the importance of healthcare data in medicine and showcases academic contributions and relevant publications. Finally, it summarizes the results of t...
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