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26191 |
Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm
Enthalten in BMC public health Bd. 22, 5.1.2022, Nr. 1, date:12.2022: 1-10
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26192 |
Predictive Diagnosis of Glaucoma Based on Analysis of Focal Notching along the Neuro-Retinal Rim Using Machine Learning
Enthalten in Pattern recognition and image analysis Bd. 29, 18.9.2019, Nr. 3, date:7.2019: 523-532
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26193 |
Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study
Enthalten in BMC medical research methodology Bd. 24, 10.9.2024, Nr. 1, date:12.2024: 1-13
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26194 |
Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique
Enthalten in BMC musculoskeletal disorders Bd. 24, 23.3.2023, Nr. 1, date:12.2023: 1-8
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26195 |
Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning
Enthalten in Scientific reports Bd. 15, 25.5.2025, Nr. 1, date:12.2025: 1-13
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26196 |
Predictive machine learning and geospatial modeling reveal PM10 hotspots and guide targeted air pollution interventions in Addis Ababa, Ethiopia
Enthalten in Discover applied sciences Bd. 7, 26.3.2025, Nr. 4, date:4.2025: 1-26
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26197 |
Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich ataxia
Enthalten in Scientific reports Bd. 15, 21.5.2025, Nr. 1, date:12.2025: 1-13
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26198 |
Predictive machine learning approaches for the microstructural behavior of multiphase zirconium alloys
Enthalten in Scientific reports Bd. 13, 3.4.2023, Nr. 1, date:12.2023: 1-12
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26199 |
Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey
Enthalten in Scientific reports Bd. 14, 21.10.2024, Nr. 1, date:12.2024: 1-9
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26200 |
Predictive maintenance of abnormal wind turbine events by using machine learning based on condition monitoring for anomaly detection
Enthalten in Journal of mechanical science and technology Bd. 35, 8.12.2021, Nr. 12, date:12.2021: 5323-5333
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