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28001 |
Using machine learning to determine the time of exposure to infection by a respiratory pathogen
Enthalten in Scientific reports Bd. 13, 1.4.2023, Nr. 1, date:12.2023: 1-10
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28002 |
Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study
Enthalten in BMC medical informatics and decision making Bd. 23, 11.6.2023, Nr. 1, date:12.2023: 1-11
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28003 |
Using machine learning to develop a five-item short form of the children’s depression inventory
Enthalten in BMC public health Bd. 24, 23.4.2024, Nr. 1, date:12.2024: 1-11
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28004 |
Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases
Enthalten in Scientific reports Bd. 14, 19.11.2024, Nr. 1, date:12.2024: 1-11
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28005 |
Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions
Enthalten in World journal of surgery Bd. 46, 26.10.2021, Nr. 2, date:2.2022: 339-346
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28006 |
Using machine learning to estimate the incidence rate of intimate partner violence
Enthalten in Scientific reports Bd. 13, 4.4.2023, Nr. 1, date:12.2023: 1-9
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28007 |
Using machine learning to examine the relationship between asthma and absenteeism
Enthalten in Environmental monitoring and assessment Bd. 191, 28.6.2019, Nr. 2, date:6.2019: 1-9
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28008 |
Using machine learning to explore core risk factors associated with the risk of eating disorders among non-clinical young women in China: A decision-tree classification analysis
Enthalten in Journal of eating disorders Bd. 10, 10.2.2022, Nr. 1, date:12.2022: 1-11
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28009 |
Using machine learning to explore the determinants of service satisfaction with online healthcare platforms during the COVID-19 pandemic
Enthalten in Service business Bd. 17, 16.5.2023, Nr. 2, date:6.2023: 449-476
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28010 |
Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand
Enthalten in Scientific reports Bd. 15, 25.2.2025, Nr. 1, date:12.2025: 1-10
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Administration
