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Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

16701 - 16710 von 16964
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Artikel 16701 Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis
Enthalten in Molecular medicine Bd. 29, 20.3.2023, Nr. 1, date:12.2023: 1-16
Online Ressource
Artikel 16702 Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population
Enthalten in BMC pulmonary medicine Bd. 22, 29.8.2022, Nr. 1, date:12.2022: 1-11
Online Ressource
Artikel 16703 Use of machine learning techniques for identifying ischemic stroke instead of the rule-based methods: a nationwide population-based study
Enthalten in European journal of medical research Bd. 29, 3.1.2024, Nr. 1, date:12.2024: 1-9
Online Ressource
Artikel 16704 Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa
Enthalten in BMC medical research methodology Bd. 21, 31.7.2021, Nr. 1, date:12.2021: 1-11
Online Ressource
Artikel 16705 Use of machine learning to analyse routinely collected intensive care unit data: a systematic review
Enthalten in Critical care Bd. 23, 22.8.2019, Nr. 1, date:12.2019: 1-11
Online Ressource
Artikel 16706 Use of machine learning to assess factors affecting progression, retention, and graduation in first-year health professions students in Qatar: a longitudinal study
Enthalten in BMC medical education Bd. 23, 30.11.2023, Nr. 1, date:12.2023: 1-11
Online Ressource
Artikel 16707 Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales
Enthalten in Scientific reports Bd. 10, 2.11.2020, Nr. 1, date:12.2020: 1-10
Online Ressource
Artikel 16708 Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit
Enthalten in Scientific reports Bd. 9, 11.3.2019, Nr. 1, date:12.2019: 1-18
Online Ressource
Artikel 16709 Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device
Enthalten in BMC medical informatics and decision making Bd. 22, 6.7.2022, Nr. 1, date:12.2022: 1-12
Online Ressource
Artikel 16710 Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T
Enthalten in Scientific reports Bd. 13, 15.5.2023, Nr. 1, date:12.2023: 1-17
Online Ressource


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