Add survex paper (#104)

20-research.Rmd

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+<a href="https://doi.org/10.1093/bioinformatics/btad723">survex: an R package for explaining machine learning survival models</a>
+<p> Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein, Hubert Baniecki, Marvin N Wright, Przemysław Biecek</p>
+<p><strong>Bioinformatics (2023)</strong></p>
+This paper demonstrates the functionalities of the <a href="https://github.com/ModelOriented/survex">survex package</a>, which provides a comprehensive set of tools for explaining machine learning survival models. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, promoting transparency and responsibility in sensitive areas.
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 <img src="images/consolidated-learning.png">
 <a href="https://doi.org/10.1007/s10994-023-06359-0">Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks</a>