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10921 |
Predicting mortality among septic patients presenting to the emergency department–a cross sectional analysis using machine learning
Enthalten in BMC emergency medicine Bd. 21, 12.7.2021, Nr. 1, date:12.2021: 1-8
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10922 |
Predicting mortality and re-hospitalization for heart failure: a machine-learning and cluster analysis on frailty and comorbidity
Enthalten in Aging clinical and experimental research Bd. 35, 18.10.2023, Nr. 12, date:12.2023: 2919-2928
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10923 |
Predicting mortality and risk factors of sepsis related ARDS using machine learning models
Enthalten in Scientific reports Bd. 15, 18.4.2025, Nr. 1, date:12.2025: 1-12
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10924 |
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes
Enthalten in BMC medical informatics and decision making Bd. 20, 30.12.2020, Nr. 11, date:12.2020: 1-7
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10925 |
Predicting mortality in the very old: a machine learning analysis on claims data
Enthalten in Scientific reports Bd. 12, 19.10.2022, Nr. 1, date:12.2022: 1-9
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10926 |
Predicting mother and newborn skin-to-skin contact using a machine learning approach
Enthalten in BMC pregnancy and childbirth Bd. 25, 18.2.2025, Nr. 1, date:12.2025: 1-7
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10927 |
Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review
Enthalten in Journal of neurology Bd. 271, 12.9.2024, Nr. 10, date:10.2024: 6543-6572
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10928 |
Predicting multiple taste sensations with a multiobjective machine learning method
Enthalten in npj science of food Bd. 8, 25.7.2024, Nr. 1, date:12.2024: 1-10
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10929 |
Predicting neurodevelopmental disorders using machine learning models and electronic health records – status of the field
Enthalten in Journal of neurodevelopmental disorders Bd. 16, 15.11.2024, Nr. 1, date:12.2024: 1-15
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10930 |
Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data
Enthalten in BMC pregnancy and childbirth Bd. 24, 17.9.2024, Nr. 1, date:12.2024: 1-13
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Administration
