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Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunit...

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Autors principals: Inna Kolyshkina, Simeon Simoff
Format: Artigo
Idioma:Inglês
Publicat: Frontiers Media S.A. 2021-05-01
Col·lecció:Frontiers in Big Data
Matèries:
machine learning
interpretability
public health
data science methodology
CRISP-ML
necessary level of interpretability
Accés en línia:https://www.frontiersin.org/articles/10.3389/fdata.2021.660206/full
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