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16151 |
The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory
Enthalten in BMC medical ethics Bd. 22, 19.8.2021, Nr. 1, date:12.2021: 1-9
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16152 |
THE EXPLAINABILITY OF GRADIENT-BOOSTED DECISION TREES FOR DIGITAL ELEVATION MODEL (DEM) ERROR PREDICTION
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-M-3-2023, 2023: 161-168. 8 S.
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16153 |
The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches
Enthalten in Arthritis Research & Therapy Bd. 21, 9.11.2019, Nr. 1, date:12.2019: 1-10
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16154 |
The Firm Life Cycle Forecasting Model Using Machine Learning Based on News Articles
Enthalten in International journal of networked and distributed computing Bd. 9, 5.1.2021, Nr. 1, date:1.2021: 1-9
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16155 |
The framing of time-dependent machine learning models improves risk estimation among young individuals with acute coronary syndromes
Enthalten in Scientific reports Bd. 13, 19.1.2023, Nr. 1, date:12.2023: 1-14
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16156 |
The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium
Enthalten in Molecular psychiatry Bd. 28, 2.5.2023, Nr. 10, date:10.2023: 4307-4319
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16157 |
The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications
Enthalten in Communications chemistry Bd. 7, 12.6.2024, Nr. 1, date:12.2024: 1-11
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16158 |
The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches
Enthalten in Discover sustainability Bd. 6, 7.2.2025, Nr. 1, date:12.2025: 1-24
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16159 |
The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach
Enthalten in Neurological sciences Bd. 41, 28.10.2019, Nr. 2, date:2.2020: 459-462
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16160 |
The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation
Enthalten in Implementation science Bd. 12, 18.10.2017, Nr. 1, date:12.2017: 1-12
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Administration
