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17751 |
Using machine learning to predict subsequent events after EMS non-conveyance decisions
Enthalten in BMC medical informatics and decision making Bd. 22, 23.6.2022, Nr. 1, date:12.2022: 1-12
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17752 |
Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study
Enthalten in BMC psychiatry Bd. 24, 2.12.2024, Nr. 1, date:12.2024: 1-11
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17753 |
Using machine learning to predict the risk of short-term and long-term death in acute kidney injury patients after commencing CRRT
Enthalten in BMC nephrology Bd. 25, 30.7.2024, Nr. 1, date:12.2024: 1-10
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17754 |
Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty
Enthalten in Scientific reports Bd. 13, 7.2.2023, Nr. 1, date:12.2023: 1-11
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17755 |
Using machine learning to simultaneously quantify multiple cognitive components of episodic memory
Enthalten in Nature Communications Bd. 16, 24.3.2025, Nr. 1, date:12.2025: 1-14
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17756 |
Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo
Enthalten in BMC bioinformatics Bd. 11, 11.2.2010, Nr. 1, date:12.2010: 1-13
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17757 |
Using machine learning to understand social isolation and loneliness in schizophrenia, bipolar disorder, and the community
Enthalten in Schizophrenia Bd. 10, 5.10.2024, Nr. 1, date:12.2024: 1-8
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17758 |
Using machine learning tools for protein database biocuration assistance
Enthalten in Scientific reports Bd. 8, 5.7.2018, Nr. 1, date:12.2018: 1-10
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17759 |
Using machine learning tools to predict outcomes for emergency department intensive care unit patients
Enthalten in Scientific reports Bd. 10, 1.12.2020, Nr. 1, date:12.2020: 1-10
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17760 |
Using machine learning with case studies to identify practices that reduce greenhouse gas emissions across Australian grain production regions
Enthalten in Agronomy for sustainable development Bd. 43, 20.3.2023, Nr. 2, date:4.2023: 1-11
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Administration
