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27781 |
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
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27782 |
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
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27783 |
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
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27784 |
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
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27785 |
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
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27786 |
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
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27787 |
Use of machine learning to diagnose breast cancer from raw electrical impedance tomography data
Enthalten in Biomedical engineering Bd. 58, 17.9.2024, Nr. 3, date:9.2024: 208-212
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27788 |
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
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27789 |
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
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27790 |
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
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Administration
