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10991 |
Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm
Enthalten in BMC medical informatics and decision making Bd. 22, 22.9.2022, Nr. 1, date:12.2022: 1-11
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10992 |
Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach
Enthalten in Translational Psychiatry Bd. 8, 5.11.2018, Nr. 1, date:12.2018: 1-11
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10993 |
Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches
Enthalten in Scientific reports Bd. 10, 1.12.2020, Nr. 1, date:12.2020: 1-8
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10994 |
Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?
Enthalten in Scientific reports Bd. 7, 15.6.2017, Nr. 1, date:12.2017: 1-9
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10995 |
Predicting the presence of depressive symptoms in the HIV-HCV co-infected population in Canada using supervised machine learning
Enthalten in BMC medical research methodology Bd. 22, 12.8.2022, Nr. 1, date:12.2022: 1-11
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10996 |
Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
Enthalten in npj computational materials Bd. 6, 15.12.2020, Nr. 1, date:12.2020: 1-12
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10997 |
Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences
Enthalten in Biomed Central (London): BMC Research Notes Bd. 17, 9.8.2024, Nr. 1, date:12.2024: 1-6
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10998 |
Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences
Enthalten in Scientific reports Bd. 14, 19.8.2024, Nr. 1, date:12.2024: 1-12
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10999 |
Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand
Enthalten in BMC medical informatics and decision making Bd. 24, 27.9.2024, Nr. 1, date:12.2024: 1-13
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|
11000 |
Predicting the risk of inappropriate depth of endotracheal intubation in pediatric patients using machine learning approaches
Enthalten in Scientific reports Bd. 13, 29.3.2023, Nr. 1, date:12.2023: 1-9
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Administration
