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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.15218 |
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| _version_ | 1866910147871244288 |
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| author | Ouyang, Han Singhal, Ayush Hamilton, Jesse Amal, Saeed |
| author_facet | Ouyang, Han Singhal, Ayush Hamilton, Jesse Amal, Saeed |
| contents | The stacking ensemble combining RF, LightGBM, and DNN performed well on internal test sets, exhibiting an NPV greater than 99.9% even with substantial class imbalance. While performance was lower on the external eICU cohort compared to the internal test sets, sensitivity remained robust. Therefore, the stacking ensemble may serve as a rule-out screening option for ERs and ICUs after additional prospective multi-site validation studies for its efficacy in real-world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ensemble Deep Learning Models for Early Detection of Meningitis in ICU: Multi-center Study Ouyang, Han Singhal, Ayush Hamilton, Jesse Amal, Saeed Machine Learning The stacking ensemble combining RF, LightGBM, and DNN performed well on internal test sets, exhibiting an NPV greater than 99.9% even with substantial class imbalance. While performance was lower on the external eICU cohort compared to the internal test sets, sensitivity remained robust. Therefore, the stacking ensemble may serve as a rule-out screening option for ERs and ICUs after additional prospective multi-site validation studies for its efficacy in real-world. |
| title | Ensemble Deep Learning Models for Early Detection of Meningitis in ICU: Multi-center Study |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.15218 |