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25491 |
Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making
Enthalten in Scientific reports Bd. 13, 21.8.2023, Nr. 1, date:12.2023: 1-16
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25492 |
Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model
Enthalten in BMC medical informatics and decision making Bd. 22, 16.5.2022, Nr. 1, date:12.2022: 1-7
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25493 |
Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach
Enthalten in Journal of youth and adolescence Bd. 52, 19.4.2023, Nr. 8, date:8.2023: 1595-1619
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25494 |
Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning
Enthalten in Environmental science and pollution research Bd. 30, 27.9.2023, Nr. 50, date:10.2023: 108846-108854
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25495 |
Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach
Enthalten in Scientific reports Bd. 11, 14.1.2021, Nr. 1, date:12.2021: 1-9
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25496 |
Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques
Enthalten in BMC public health Bd. 24, 29.7.2024, Nr. 1, date:12.2024: 1-16
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25497 |
Predicting adverse drug events in older inpatients: a machine learning study
Enthalten in International journal of clinical pharmacy Bd. 44, 17.9.2022, Nr. 6, date:12.2022: 1304-1311
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25498 |
Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
Enthalten in BMC pregnancy and childbirth Bd. 23, 2.8.2023, Nr. 1, date:12.2023: 1-14
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25499 |
Predicting all-cause mortality with machine learning among Brazilians aged 50 and over: results from The Brazilian Longitudinal Study of Ageing (ELSI-Brazil)
Enthalten in npj aging Bd. 11, 28.3.2025, Nr. 1, date:12.2025: 1-8
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25500 |
Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire
Enthalten in Fire technology Bd. 59, 22.1.2023, Nr. 2, date:3.2023: 793-825
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Administration
