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Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling

Scientists all over the world are moving toward building database systems based on the One Health concept to prevent and manage outbreaks of zoonotic diseases. An appreciation of the process of discovery with incomplete information and a recognition of the role of observations gathered painstakingly...

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Detalles Bibliográficos
Autores principales: Pandit, Nitin, Vanak, Abi T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541757/
https://www.ncbi.nlm.nih.gov/pubmed/33046950
http://dx.doi.org/10.1007/s41745-020-00192-3
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author Pandit, Nitin
Vanak, Abi T.
author_facet Pandit, Nitin
Vanak, Abi T.
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collection PubMed
description Scientists all over the world are moving toward building database systems based on the One Health concept to prevent and manage outbreaks of zoonotic diseases. An appreciation of the process of discovery with incomplete information and a recognition of the role of observations gathered painstakingly by scientists in the field shows that simple databases will not be sufficient to build causal models of the complex relationships between human health and ecosystems. Rather, it is important also to build knowledge bases which complement databases using non-monotonic logic based artificial intelligence techniques, so that causal models can be improved as new, and sometimes contradictory, information is found from field studies.
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spelling pubmed-75417572020-10-08 Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling Pandit, Nitin Vanak, Abi T. J Indian Inst Sci Review Article Scientists all over the world are moving toward building database systems based on the One Health concept to prevent and manage outbreaks of zoonotic diseases. An appreciation of the process of discovery with incomplete information and a recognition of the role of observations gathered painstakingly by scientists in the field shows that simple databases will not be sufficient to build causal models of the complex relationships between human health and ecosystems. Rather, it is important also to build knowledge bases which complement databases using non-monotonic logic based artificial intelligence techniques, so that causal models can be improved as new, and sometimes contradictory, information is found from field studies. Springer India 2020-10-08 2020 /pmc/articles/PMC7541757/ /pubmed/33046950 http://dx.doi.org/10.1007/s41745-020-00192-3 Text en © Indian Institute of Science 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Review Article
Pandit, Nitin
Vanak, Abi T.
Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling
title Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling
title_full Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling
title_fullStr Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling
title_full_unstemmed Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling
title_short Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling
title_sort artificial intelligence and one health: knowledge bases for causal modeling
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541757/
https://www.ncbi.nlm.nih.gov/pubmed/33046950
http://dx.doi.org/10.1007/s41745-020-00192-3
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