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With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications.
The robust inference for the cox proportional hazards model
Lin, D. Y. and Wei, L.-J · 1989
Earlier work this paper cites.
Survival analysis with median regression models
Ying, Z., Jung, S.-H., and Wei, L.-J · 1995
Earlier work this paper cites.
Decade-long trends and factors associated with time to hospital presentation in patients with acute myocardial infarction: the worcester heart attack study
Goldberg, R. J., Yarzebski, J., Lessard, D., and Gore, J. M · 2000
Earlier work this paper cites.
Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the transbig multicenter independent validation series
Desmedt, C., Piette, F., Loi, S., Wang, Y., Lallemand, F., Haibe-Kains, B., Viale, G., Delorenzi, M., Zhang, Y., d’Assignies, M. S., et al · 2007
Earlier work this paper cites.
Survival methods
Rao, S. R. and Schoenfeld, D. A · 2007
Earlier work this paper cites.
Random survival forests
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., and Lauer, M. S · 2008
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. and Lee, S.-I · 2017
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Aas, K., Jullum, M., and Løland, A · 2019
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Measurable counterfactual local explanations for any classifier
White, A. and Garcez, A. d · 2019
Cited alongside, same era.
Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al · 2020
Cited alongside, same era.
Survlime: A method for explaining machine learning survival models
Kovalev, M. S., Utkin, L. V., and Kasimov, E. M · 2020
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The explanation game: Explaining machine learning models using shapley values
Merrick, L. and Taly, A · 2020
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The many shapley values for model explanation
Sundararajan, M. and Najmi, A · 2020
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Utkin, L. V., Kovalev, M. S., and Kasimov, E. M · 2020
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Shapley values for feature selection: The good, the bad, and the axioms
Fryer, D., Strümke, I., and Nguyen, H · 2021
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Frye, C., de Mijolla, D., Cowton, L., Stanley, M., and Feige, I · 2020
Cited alongside, same era.
Feature relevance quantification in explainable ai: A causal problem
Janzing, D., Minorics, L., and Blöbaum, P · 2020
Cited alongside, same era.