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...

Descrizione completa

Dettagli Bibliografici

Autori principali:	Inna Kolyshkina, Simeon Simoff
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2021-05-01
Serie:	Frontiers in Big Data
Soggetti:	machine learning interpretability public health data science methodology CRISP-ML necessary level of interpretability
Accesso online:	https://www.frontiersin.org/articles/10.3389/fdata.2021.660206/full
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!