Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions
Hospital readmissions impose a significant financial strain on healthcare systems and can adversely affect patients. Unfortunately, traditional approaches to predicting readmissions frequently lack accuracy. This presents a critical challenge, as identifying patients at high risk for readmission is...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/acis/7182123 |
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| _version_ | 1850182329174065152 |
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| author | Odai Mohammad Al-Jbour Mohammad Alshraideh Bahaaldeen Alshraideh |
| author_facet | Odai Mohammad Al-Jbour Mohammad Alshraideh Bahaaldeen Alshraideh |
| author_sort | Odai Mohammad Al-Jbour |
| collection | DOAJ |
| description | Hospital readmissions impose a significant financial strain on healthcare systems and can adversely affect patients. Unfortunately, traditional approaches to predicting readmissions frequently lack accuracy. This presents a critical challenge, as identifying patients at high risk for readmission is essential for implementing preventive measures. The study introduces a novel method that employs machine learning to automatically extract features from patient data, eliminating labor-intensive manual feature engineering. The primary goal is to develop predictive models for unplanned readmissions for UTI patients at Jordan University Hospital within 3 months postdischarge. This is executed through a retrospective analysis of electronic health records from January 2020 to June 2023. By leveraging machine learning techniques, the study identifies high-risk patients by evaluating demographic, clinical, and outcome characteristics, ensuring model reliability through thorough optimization, validation, and performance assessment. Three predictive models were developed as follows: a gradient-boosting classifier (GBC), logistic regression (LR), and stochastic gradient descent (SGD). The GBC, SGD, and LR achieved impressive accuracy rates of 99%, 95%, and 89%, providing strong confidence in the methodology. The study’s findings reveal key risk factors associated with readmissions, enhancing our understanding of this process and offering a valuable framework for improving patient care, optimizing resource allocation, and supporting evidence-based decision-making in healthcare management. |
| format | Article |
| id | doaj-art-e8ce61e4ec6a4535bc563652e324a321 |
| institution | OA Journals |
| issn | 1687-9732 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-e8ce61e4ec6a4535bc563652e324a3212025-08-20T02:17:39ZengWileyApplied Computational Intelligence and Soft Computing1687-97322025-01-01202510.1155/acis/7182123Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce ReadmissionsOdai Mohammad Al-Jbour0Mohammad Alshraideh1Bahaaldeen Alshraideh2Computer Science DepartmentArtificial Intelligence DepartmentDivision of UrologyHospital readmissions impose a significant financial strain on healthcare systems and can adversely affect patients. Unfortunately, traditional approaches to predicting readmissions frequently lack accuracy. This presents a critical challenge, as identifying patients at high risk for readmission is essential for implementing preventive measures. The study introduces a novel method that employs machine learning to automatically extract features from patient data, eliminating labor-intensive manual feature engineering. The primary goal is to develop predictive models for unplanned readmissions for UTI patients at Jordan University Hospital within 3 months postdischarge. This is executed through a retrospective analysis of electronic health records from January 2020 to June 2023. By leveraging machine learning techniques, the study identifies high-risk patients by evaluating demographic, clinical, and outcome characteristics, ensuring model reliability through thorough optimization, validation, and performance assessment. Three predictive models were developed as follows: a gradient-boosting classifier (GBC), logistic regression (LR), and stochastic gradient descent (SGD). The GBC, SGD, and LR achieved impressive accuracy rates of 99%, 95%, and 89%, providing strong confidence in the methodology. The study’s findings reveal key risk factors associated with readmissions, enhancing our understanding of this process and offering a valuable framework for improving patient care, optimizing resource allocation, and supporting evidence-based decision-making in healthcare management.http://dx.doi.org/10.1155/acis/7182123 |
| spellingShingle | Odai Mohammad Al-Jbour Mohammad Alshraideh Bahaaldeen Alshraideh Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions Applied Computational Intelligence and Soft Computing |
| title | Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions |
| title_full | Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions |
| title_fullStr | Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions |
| title_full_unstemmed | Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions |
| title_short | Precision Healthcare for UTIs: Leveraging Machine Learning to Reduce Readmissions |
| title_sort | precision healthcare for utis leveraging machine learning to reduce readmissions |
| url | http://dx.doi.org/10.1155/acis/7182123 |
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