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OrthoMortPred: predicting one-year mortality following orthopedic hospitalization

2024-12Journal article Open access
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dc.contributor.authorPires de Carvalho, Filipe Ricardo
dc.contributor.authorGavaia, Paulo
dc.contributor.authorBrito Camacho, António
dc.date.accessioned2025-01-14T10:31:52Z
dc.date.available2025-01-14T10:31:52Z
dc.date.issued2024-12
dc.description.abstractObjective: Predicting mortality risk following orthopedic surgery is crucial for informed decision-making and patient care. This study aims to develop and validate a machine learning model for predicting one-year mortality risk after orthopedic hospitalization and to create a personalized risk prediction tool for clinical use. Methods: We analyzed data from 3,132 patients who underwent orthopedic procedures at the Central Lisbon University Hospital Center from 2021 to 2023. Using the LightGBM algorithm, we developed a predictive model incorporating various clinical and administrative variables. We employed SHAP (SHapley Additive exPlanations) values for model interpretation and created a personalized risk prediction tool for individual patient assessment. Results: Our model achieved an accuracy of 93% and an area under the ROC curve of 0.93 for predicting one-year mortality. Notably, ’EMERGENCY ADMISSION DATE TIME’ emerged as the most influential predictor, followed by age and pre-operative days. The model demonstrated robust performance across different patient subgroups and outperformed traditional statistical methods. The personalized risk prediction tool provides clinicians with real-time, patient-specific risk assessments and insights into contributing factors. Conclusion: Our study presents a highly accurate model for predicting one-year mortality following orthopedic hospitalization. The significance of ’EMERGENCY ADMISSION DATE TIME’ as the primary predictor highlights the importance of admission timing in patient outcomes. The accompanying personalized risk prediction tool offers a practical means of implementing this model in clinical settings, potentially improving risk stratification and patient care in orthopedic practice.eng
dc.identifier.doi10.1016/j.ijmedinf.2024.105657
dc.identifier.issn1386-5056
dc.identifier.urihttp://hdl.handle.net/10400.1/26620
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationAlgarve Centre for Marine Sciences
dc.relationAlgarve Centre for Marine Sciences
dc.relationCentre for Marine and Environmental Research
dc.relation.ispartofInternational Journal of Medical Informatics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectOrthopedics
dc.subjectMortality
dc.subjectLightGBM
dc.subjectPrediction
dc.subjectSHAP
dc.titleOrthoMortPred: predicting one-year mortality following orthopedic hospitalizationeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleCentre for Marine and Environmental Research
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0101%2F2020/PT
oaire.citation.startPage105657
oaire.citation.titleInternational Journal of Medical Informatics
oaire.citation.volume192
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePires de Carvalho
person.familyNameGavaia
person.familyNameBrito Camacho
person.givenNameFilipe Ricardo
person.givenNamePaulo
person.givenNameAntónio
person.identifier.ciencia-id181A-A440-7D6E
person.identifier.ciencia-idB619-FC16-D007
person.identifier.ciencia-id921B-3113-3EBE
person.identifier.orcid0000-0002-1468-0305
person.identifier.orcid0000-0002-9582-1957
person.identifier.orcid0000-0002-7914-4538
person.identifier.ridA-6470-2011
person.identifier.scopus-author-id55115644400
person.identifier.scopus-author-id6507104377
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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