Hospital readmission risk
| dc.contributor.advisor | Salau, Sulaiman | |
| dc.contributor.advisor | Er, Sebnem | |
| dc.contributor.author | Mugova, Amos | |
| dc.date.accessioned | 2025-03-05T11:11:08Z | |
| dc.date.available | 2025-03-05T11:11:08Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2025-03-05T09:26:06Z | |
| dc.description.abstract | Hospital readmissions are a significant challenge in healthcare, as they lead to in creased costs, higher risk of mortality, treatment complications, and patient dis tress. This minor dissertation, set within the South African healthcare framework, investigates the potential of both traditional clinical screening tools and advanced statistical learning methods for predicting hospital readmission risk. The meth ods considered include the LACE score, decision trees, logistic regression, random forests, gradient-boosting methods, and neural networks. The study uses data from South Africa's privately insured demographic, provided by a private insurer. It includes a comprehensive array of patient information such as demographics, prescribed medications, medical procedures undergone, and historical hospital usage. Feature selection methods were used to identify relevant variables for model training, and the effectiveness of these variables was assessed based on their ability to differentiate between patients at risk of hospital readmission within 30 days after discharge. The statistical learning methods' efficacy was measured using several performance indicators, such as prediction accuracy, F1 score, Area Under the Receiver Operating Characteristics Curve (AUC), Area Under the Precision-Recall Curve (AUC-PR), and the Matthews Correlation Coefficient (MCC). The study found that the neural network model outperformed the other statistical learning methods evaluated across various metrics. Moreover, the research extends the range of variables used to predict hospital read missions beyond the traditional LACE score, incorporating critical factors such as the frequency and costs of previous hospital visits, expenses related to specialist services, patient age, and the primary diagnosis category. | |
| dc.identifier.apacitation | Mugova, A. (2024). <i>Hospital readmission risk</i>. (). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/41108 | en_ZA |
| dc.identifier.chicagocitation | Mugova, Amos. <i>"Hospital readmission risk."</i> ., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2024. http://hdl.handle.net/11427/41108 | en_ZA |
| dc.identifier.citation | Mugova, A. 2024. Hospital readmission risk. . University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/41108 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Mugova, Amos AB - Hospital readmissions are a significant challenge in healthcare, as they lead to in creased costs, higher risk of mortality, treatment complications, and patient dis tress. This minor dissertation, set within the South African healthcare framework, investigates the potential of both traditional clinical screening tools and advanced statistical learning methods for predicting hospital readmission risk. The meth ods considered include the LACE score, decision trees, logistic regression, random forests, gradient-boosting methods, and neural networks. The study uses data from South Africa's privately insured demographic, provided by a private insurer. It includes a comprehensive array of patient information such as demographics, prescribed medications, medical procedures undergone, and historical hospital usage. Feature selection methods were used to identify relevant variables for model training, and the effectiveness of these variables was assessed based on their ability to differentiate between patients at risk of hospital readmission within 30 days after discharge. The statistical learning methods' efficacy was measured using several performance indicators, such as prediction accuracy, F1 score, Area Under the Receiver Operating Characteristics Curve (AUC), Area Under the Precision-Recall Curve (AUC-PR), and the Matthews Correlation Coefficient (MCC). The study found that the neural network model outperformed the other statistical learning methods evaluated across various metrics. Moreover, the research extends the range of variables used to predict hospital read missions beyond the traditional LACE score, incorporating critical factors such as the frequency and costs of previous hospital visits, expenses related to specialist services, patient age, and the primary diagnosis category. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - data science LK - https://open.uct.ac.za PB - University of Cape Town PY - 2024 T1 - Hospital readmission risk TI - Hospital readmission risk UR - http://hdl.handle.net/11427/41108 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/41108 | |
| dc.identifier.vancouvercitation | Mugova A. Hospital readmission risk. []. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41108 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | data science | |
| dc.title | Hospital readmission risk | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |