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10941 |
Predicting rice blast disease: machine learning versus process-based models
Enthalten in BMC bioinformatics Bd. 20, 22.10.2019, Nr. 1, date:12.2019: 1-16
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10942 |
Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital
Enthalten in European journal of medical research Bd. 27, 28.10.2022, Nr. 1, date:12.2022: 1-13
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10943 |
Predicting risk of stillbirth and preterm pregnancies with machine learning
Enthalten in Health Information Science and Systems Bd. 8, 25.3.2020, Nr. 1, date:12.2020: 1-12
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10944 |
Predicting rock mass strength from drilling data using synergistic unsupervised and supervised machine learning approaches
Enthalten in Earth science informatics Bd. 18, 12.3.2025, Nr. 3, date:9.2025: 1-15
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10945 |
Predicting saturated hydraulic conductivity from particle size distributions using machine learning
Enthalten in Stochastic environmental research and risk assessment Bd. 39, 19.1.2025, Nr. 2, date:2.2025: 423-435
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10946 |
Predicting self-perceived general health status using machine learning: an external exposome study
Enthalten in BMC public health Bd. 23, 31.5.2023, Nr. 1, date:12.2023: 1-17
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10947 |
Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers
Enthalten in European journal of medical research Bd. 29, 6.3.2024, Nr. 1, date:12.2024: 1-15
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10948 |
Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis
Enthalten in BMC infectious diseases Bd. 23, 27.9.2023, Nr. 1, date:12.2023: 1-21
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10949 |
Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database
Enthalten in Scientific reports Bd. 14, 15.5.2024, Nr. 1, date:12.2024: 1-9
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10950 |
Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians
Enthalten in BMC emergency medicine Bd. 24, 18.6.2024, Nr. 1, date:12.2024: 1-11
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