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11711 |
The triglyceride–glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis
Enthalten in Cardiovascular diabetology Bd. 24, 29.1.2025, Nr. 1, date:12.2025: 1-13
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11712 |
The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial
Enthalten in Trials Bd. 20, 11.10.2019, Nr. 1, date:12.2019: 1-9
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11713 |
The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
Enthalten in Journal of medical case reports Bd. 16, 12.11.2022, Nr. 1, date:12.2022: 1-6
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11714 |
The use of cloud based machine learning to predict outcome in intracerebral haemorrhage without explicit programming expertise
Enthalten in Neurosurgical review Bd. 47, 3.12.2024, Nr. 1, date:12.2024: 1-9
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11715 |
The Use of Fitness-Fatigue Models for Sport Performance Modelling: Conceptual Issues and Contributions from Machine-Learning
Enthalten in Sports medicine - open Bd. 8, 3.3.2022, Nr. 1, date:12.2022: 1-6
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11716 |
The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke
Enthalten in Journal of neuroEngineering and rehabilitation Bd. 20, 27.1.2023, Nr. 1, date:12.2023: 1-18
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11717 |
The use of machine learning in rare diseases: a scoping review
Enthalten in Orphanet journal of rare diseases Bd. 15, 9.6.2020, Nr. 1, date:12.2020: 1-10
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11718 |
The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors
Enthalten in Scientific reports Bd. 12, 5.11.2022, Nr. 1, date:12.2022: 1-14
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11719 |
The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models
Enthalten in Scientific reports Bd. 13, 14.11.2023, Nr. 1, date:12.2023: 1-15
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11720 |
The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities
Enthalten in Current environmental health reports Bd. 10, 17.12.2022, Nr. 1, date:3.2023: 45-60
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Administration
