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Recent efforts in Machine Learning (ML) interpretability have focused on creating methods for explaining black-box ML models.
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W. R. Zame, J. Yoon, F. W. Asselbergs, and M. van der Schaar, “Interpretable machine learning identifies risk predictors in patients with heart failure,” American Heart Association (AHA) Scientific Sessions
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2018
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2018
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Accessed: 2018-09-26
“Machine learning in prognostic decision-support systems study.” https://mlindecisionsupport.github.io/ · 2018
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Accessed: 2018-09-26
“Machine learning in prognostic decision-support systems study - computer science perspective.” https://mlinterpretability.github.io/ · 2018
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