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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2602.02930 |
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| _version_ | 1866917244241444864 |
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| author | Jin, Yin Stewart, Tucker R. Zhou, Deyi Gupta, Chhavi Nema, Arjita Brakenridge, Scott C. O'Keefe, Grant E. Hu, Juhua |
| author_facet | Jin, Yin Stewart, Tucker R. Zhou, Deyi Gupta, Chhavi Nema, Arjita Brakenridge, Scott C. O'Keefe, Grant E. Hu, Juhua |
| contents | Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical outcome can be substantially improved through early detection and timely intervention. By leveraging publicly available datasets, machine learning (ML) has driven advances in both research and clinical practice. However, existing public datasets consider ICU patients (Intensive Care Unit) as a uniform group and neglect the potential challenges presented by critically ill trauma patients in whom injury-related inflammation and organ dysfunction can overlap with the clinical features of sepsis. We propose that a targeted identification of post-traumatic sepsis is necessary in order to develop methods for early detection. Therefore, we introduce a publicly available standardized post-trauma sepsis onset dataset extracted, relabeled using standardized post-trauma clinical facts, and validated from MIMIC-III. Furthermore, we frame early detection of post-trauma sepsis onset according to clinical workflow in ICUs in a daily basis resulting in a new rare event detection problem. We then establish a general benchmark through comprehensive experiments, which shows the necessity of further advancements using this new dataset. The data code is available at https://github.com/ML4UWHealth/SepsisOnset_TraumaCohort.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02930 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients Jin, Yin Stewart, Tucker R. Zhou, Deyi Gupta, Chhavi Nema, Arjita Brakenridge, Scott C. O'Keefe, Grant E. Hu, Juhua Machine Learning Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical outcome can be substantially improved through early detection and timely intervention. By leveraging publicly available datasets, machine learning (ML) has driven advances in both research and clinical practice. However, existing public datasets consider ICU patients (Intensive Care Unit) as a uniform group and neglect the potential challenges presented by critically ill trauma patients in whom injury-related inflammation and organ dysfunction can overlap with the clinical features of sepsis. We propose that a targeted identification of post-traumatic sepsis is necessary in order to develop methods for early detection. Therefore, we introduce a publicly available standardized post-trauma sepsis onset dataset extracted, relabeled using standardized post-trauma clinical facts, and validated from MIMIC-III. Furthermore, we frame early detection of post-trauma sepsis onset according to clinical workflow in ICUs in a daily basis resulting in a new rare event detection problem. We then establish a general benchmark through comprehensive experiments, which shows the necessity of further advancements using this new dataset. The data code is available at https://github.com/ML4UWHealth/SepsisOnset_TraumaCohort.git. |
| title | Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.02930 |