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Artikel 16331 Using machine learning for the assessment of ecological status of unmonitored waters in Poland
Enthalten in Scientific reports Bd. 14, 18.10.2024, Nr. 1, date:12.2024: 1-14
Online Ressource
Artikel 16332 Using Machine Learning Hardware to Solve Linear Partial Differential Equations with Finite Difference Methods
Enthalten in International journal of parallel programming Bd. 53, 4.3.2025, Nr. 2, date:4.2025: 1-22
Online Ressource
Artikel 16333 Using machine learning in pediatric cardio-oncology: we have the questions, we need the answers
Enthalten in Cardio-Oncology Bd. 10, 6.11.2024, Nr. 1, date:12.2024: 1-3
Online Ressource
Artikel 16334 Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care
Enthalten in BMC medicine Bd. 23, 30.4.2025, Nr. 1, date:12.2025: 1-12
Online Ressource
Artikel 16335 Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy
Enthalten in BMC gastroenterology Bd. 23, 6.4.2023, Nr. 1, date:12.2023: 1-11
Online Ressource
Artikel 16336 Using machine learning methods to predict electric vehicles penetration in the automotive market
Enthalten in Scientific reports Bd. 13, 23.5.2023, Nr. 1, date:12.2023: 1-16
Online Ressource
Artikel 16337 Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
Enthalten in BMC medical informatics and decision making Bd. 20, 2.10.2020, Nr. 1, date:12.2020: 1-10
Online Ressource
Artikel 16338 Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
Enthalten in Scientific reports Bd. 13, 22.12.2023, Nr. 1, date:12.2023: 1-13
Online Ressource
Artikel 16339 Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis
Enthalten in BMC pediatrics Bd. 25, 24.5.2025, Nr. 1, date:12.2025: 1-12
Online Ressource
Artikel 16340 Using machine learning models to improve stroke risk level classification methods of China national stroke screening
Enthalten in BMC medical informatics and decision making Bd. 19, 10.12.2019, Nr. 1, date:12.2019: 1-7
Online Ressource


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