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dc.contributor.author
Grendas, Leandro Nicolás  
dc.contributor.author
Chiapella, Luciana Carla  
dc.contributor.author
Rodante, Demian Emanuel  
dc.contributor.author
Daray, Federico Manuel  
dc.date.available
2023-02-13T13:12:51Z  
dc.date.issued
2022-01  
dc.identifier.citation
Grendas, Leandro Nicolás; Chiapella, Luciana Carla; Rodante, Demian Emanuel; Daray, Federico Manuel; Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour; Pergamon-Elsevier Science Ltd; Journal of Psychiatric Research; 145; 1-2022; 85-91  
dc.identifier.issn
0022-3956  
dc.identifier.uri
http://hdl.handle.net/11336/187733  
dc.description.abstract
Background: Despite considerable research efforts during the last five decades, the prediction of suicidal behaviour (SB) using traditional model-based statistical has been weak. This marks the need to explore new statistical methods. Objective: To compare the performance of Cox regression models versus Random Survival Forest (RSF) to predict SB. Methods: Using a data set of more than 300 high-risk suicidal patients from a multicenter prospective cohort study, we compare Cox regression models with RSF to address predictors of time to suicide reattempt. Cross-validation was used to assess model prediction performance, including the area under the receiver operator curve (AUC), precision, Integrated Brier Score (IBS), sensitivity, and specificity. Results: A variant of the RSF denominated the RSFElimin, in which irrelevant predictor variables were eliminated from the model, presented the best accuracy, sensitivity, AUC and IBS. At the same time, the sensitivity of this method was slightly lower than that obtained with the Cox regression model with all predictor variables (CoxComp). Conclusion: The RSF, a machine learning model, seems more sensitive and precise than the traditional Cox regression model in predicting suicidal behaviour.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Pergamon-Elsevier Science Ltd  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
MACHINE LEARNING  
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PREDICTION  
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RANDOM SURVIVAL FOREST  
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SUICIDAL BEHAVIOUR  
dc.subject.classification
Psiquiatría  
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Medicina Clínica  
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CIENCIAS MÉDICAS Y DE LA SALUD  
dc.title
Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-02-09T16:03:28Z  
dc.journal.volume
145  
dc.journal.pagination
85-91  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Grendas, Leandro Nicolás. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina  
dc.description.fil
Fil: Chiapella, Luciana Carla. Universidad Nacional de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Rodante, Demian Emanuel. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina  
dc.description.fil
Fil: Daray, Federico Manuel. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentina  
dc.journal.title
Journal of Psychiatric Research  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0022395621006786  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jpsychires.2021.11.029