Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2023 |
| Tipo de documento: | Dissertação |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/152910 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Predicting the Hospital Length of Stay of Patients with Femoral Neck FracturesFemoral Neck FractureMachine LearningPredictive ModelXGBoostLightGBMDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceFemoral neck fractures are debilitating and one of the most important reasons for long-standing pain in the elderly population. They are among the most frequent causes of hospital admissions in the elderly. In most cases, they tend to have longer lengths of hospital stay despite the damage it causes in this population due to immobility and isolation. With the predictions pointing at an increase in the elderly population in the years to come, it is also expected that the number of these fractures will increase in parallel with this growth, so it is crucial to find ways to respond to this tendency. This study presents two predictive models that attempt to estimate the length of stay, in time intervals (1-11 days, 12-17 days, 18-16 days, and over 27 days), in hospitals of patients with femoral neck fractures. One model is for making predictions on the day of the admission, and the other is to make predictions three days later (with more information on the patient’s condition). Another goal of this study is to understand the factors that impact the length of stay. Although different algorithms have been tested, the final models were built using XGBoost, as this algorithm presented the best performance in model accuracy and speed of training. Model 1 (day of admission) achieved an accuracy of 0.4, while model 2 (three days after admission) achieved an accuracy of 0.44. The models showed that the factors with a higher predictive impact were the number of diagnoses, the week of admission, and patient age. Although this study’s results are not deployment-grade, they encourage how machine learning can be used in this context. Since this study used only administrative data, further studies could use sociodemographic and clinical data to build models with increased performance.António, Nuno Miguel da ConceiçãoRUNHorta, Mariana Dias Suspiro Rodrigues2023-04-132026-04-13T00:00:00Z2023-04-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/152910TID:203274105enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:35:29Zoai:run.unl.pt:10362/152910Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:06.869170Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures |
| title |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures |
| spellingShingle |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures Horta, Mariana Dias Suspiro Rodrigues Femoral Neck Fracture Machine Learning Predictive Model XGBoost LightGBM |
| title_short |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures |
| title_full |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures |
| title_fullStr |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures |
| title_full_unstemmed |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures |
| title_sort |
Predicting the Hospital Length of Stay of Patients with Femoral Neck Fractures |
| author |
Horta, Mariana Dias Suspiro Rodrigues |
| author_facet |
Horta, Mariana Dias Suspiro Rodrigues |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
António, Nuno Miguel da Conceição RUN |
| dc.contributor.author.fl_str_mv |
Horta, Mariana Dias Suspiro Rodrigues |
| dc.subject.por.fl_str_mv |
Femoral Neck Fracture Machine Learning Predictive Model XGBoost LightGBM |
| topic |
Femoral Neck Fracture Machine Learning Predictive Model XGBoost LightGBM |
| description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023-04-13 2023-04-13T00:00:00Z 2026-04-13T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
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http://hdl.handle.net/10362/152910 TID:203274105 |
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http://hdl.handle.net/10362/152910 |
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TID:203274105 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
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info:eu-repo/semantics/embargoedAccess |
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embargoedAccess |
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application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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