Selecting Interpretability Techniques for Healthcare Machine Learning models
<|reference_start|>Selecting Interpretability Techniques for Healthcare Machine Learning models: In healthcare there is a pursuit for employing interpretable algorithms to assist healthcare professionals in several decision scenarios. Following the Predictive, Descriptive and Relevant (PDR) framework, the definition of interpretable machine learning as a machine-learning model that explicitly and in a simple frame determines relationships either contained in data or learned by the model that are relevant for its functioning and the categorization of models by post-hoc, acquiring interpretability after training, or model-based, being intrinsically embedded in the algorithm design. We overview a selection of eight algorithms, both post-hoc and model-based, that can be used for such purposes.<|reference_end|>
@article{sierra-botero2024selecting,
    title={Selecting Interpretability Techniques for Healthcare Machine Learning
  models},
    author={Daniel Sierra-Botero, Ana Molina-Taborda, Mario S. Vald'es-Tresanco,
  Alejandro Hern'andez-Arango, Leonardo Espinosa-Leal, Alexander Karpenko and
  Olga Lopez-Acevedo},
    journal={arXiv preprint arXiv:2406.10213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10213},
    primaryClass={cs.LG}
}