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10921 |
Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost
Enthalten in International journal of colorectal disease Bd. 37, 15.6.2022, Nr. 7, date:7.2022: 1621-1634
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10922 |
Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach
Enthalten in Breast cancer research Bd. 26, 29.10.2024, Nr. 1, date:12.2024: 1-12
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10923 |
Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms
Enthalten in Journal of cancer research and clinical oncology Bd. 150, 21.3.2024, Nr. 3, date:3.2024: 1-13
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10924 |
Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models
Enthalten in European journal of nuclear medicine and molecular imaging Bd. 50, 17.11.2022, Nr. 3, date:2.2023: 715-726
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10925 |
Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach
Enthalten in BMC medical informatics and decision making Bd. 20, 6.2.2020, Nr. 1, date:12.2020: 1-9
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10926 |
Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study
Enthalten in Journal of neuroEngineering and rehabilitation Bd. 20, 23.2.2023, Nr. 1, date:12.2023: 1-12
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10927 |
Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning
Enthalten in BMC medical informatics and decision making Bd. 19, 8.1.2019, Nr. 1, date:12.2019: 1-13
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10928 |
Predicting peak inundation depths with a physics informed machine learning model
Enthalten in Scientific reports Bd. 14, 27.6.2024, Nr. 1, date:12.2024: 1-12
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10929 |
Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome
Enthalten in BMC bioinformatics Bd. 20, 5.1.2019, Nr. 1, date:12.2019: 1-11
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10930 |
Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods
Enthalten in BMC medical informatics and decision making Bd. 22, 28.12.2022, Nr. 1, date:12.2022: 1-10
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Administration
