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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.17865 |
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| _version_ | 1866913561090981888 |
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| author | da Silva, Flavio S. Correa Sawhney, Simon |
| author_facet | da Silva, Flavio S. Correa Sawhney, Simon |
| contents | Acute kidney injury (AKI) is a serious clinical condition that affects up to 20% of hospitalised patients. AKI is associated with short term unplanned hospital readmission and post-discharge mortality risk. Patient risk and healthcare expenditures can be minimised by followup planning grounded on predictive models and machine learning. Since AKI is multi-factorial, predictive models specialised in different categories of patients can increase accuracy of predictions. In the present article we present some results following this approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Population stratification for prediction of mortality in post-AKI patients da Silva, Flavio S. Correa Sawhney, Simon Machine Learning Acute kidney injury (AKI) is a serious clinical condition that affects up to 20% of hospitalised patients. AKI is associated with short term unplanned hospital readmission and post-discharge mortality risk. Patient risk and healthcare expenditures can be minimised by followup planning grounded on predictive models and machine learning. Since AKI is multi-factorial, predictive models specialised in different categories of patients can increase accuracy of predictions. In the present article we present some results following this approach. |
| title | Population stratification for prediction of mortality in post-AKI patients |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.17865 |