Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorNorth Carolina State University (NCSU)-
Autor(es): dc.contributorMichigan State University (MSU)-
Autor(es): dc.creatorPivato, Giovana Mancilla-
Autor(es): dc.creatorda Silva, Gustavo Venâncio-
Autor(es): dc.creatorPeres, Beatriz Granetti-
Autor(es): dc.creatorLuna, Stelio Pacca Loureiro-
Autor(es): dc.creatorPairis-Garcia, Monique Danielle-
Autor(es): dc.creatorTrindade, Pedro Henrique Esteves-
Data de aceite: dc.date.accessioned2025-08-21T21:55:37Z-
Data de disponibilização: dc.date.available2025-08-21T21:55:37Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-025-91551-6-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301605-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301605-
Descrição: dc.descriptionSurgical castration of males is carried out on a large scale in the US swine industry and the pain resulting from this procedure can be assessed using the Unesp-Botucatu pig composite acute pain scale (UPAPS). We aim to propose a short version of UPAPS based on the behaviors best-ranked by a random forest algorithm. We used behavioral observations from databases of surgically castrated pre-weaned and weaned pigs. We trained a random forest algorithm using the pain-free (pre-castration) and painful (post-castration) conditions as target variable and the 17 UPAPS pain-altered behaviors as feature variables. We ranked the behaviors by their importance in diagnosing pain. The algorithm was refined using a backward step-up procedure, establishing the Short UPAPS. The predictive capacity of the original and short version of the UPAPS was estimated by the area under the curve (AUC). In refinement, the algorithm with the five best-ranked behaviors had the lowest complexity and predictive capacity equivalent to the algorithm with all behaviors. The AUC of Short UPAPS (89.62%) was statistically equivalent (p = 0.6828) to that of UPAPS (90.58%). In conclusion, the proposed Short UPAPS might facilitate the implementation of a standard operating procedure to monitor and diagnose acute pain post-castration in large-scale systems.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionLaboratory of Applied Artificial Intelligence in Health Department of Anesthesiology Botucatu Medical School São Paulo State University (Unesp), São Paulo-
Descrição: dc.descriptionDepartment of Veterinary Surgery and Animal Reproduction School of Veterinary Medicine and Animal Science São Paulo State University (Unesp), São Paulo-
Descrição: dc.descriptionGlobal Production Animal Welfare Laboratory Department of Population Health and Pathobiology College of Veterinary Medicine North Carolina State University (NCSU)-
Descrição: dc.descriptionDepartment of Large Animal Clinical Sciences College of Veterinary Medicine Michigan State University (MSU)-
Descrição: dc.descriptionLaboratory of Applied Artificial Intelligence in Health Department of Anesthesiology Botucatu Medical School São Paulo State University (Unesp), São Paulo-
Descrição: dc.descriptionDepartment of Veterinary Surgery and Animal Reproduction School of Veterinary Medicine and Animal Science São Paulo State University (Unesp), São Paulo-
Descrição: dc.descriptionCAPES: 001-
Descrição: dc.descriptionCAPES: 002-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleProposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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