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Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease
Affiliation:1. Guangzhou Women and Children''s Medical Center, Institute of Pediatrics, Guangzhou Medical University, No.9 Jinsui Road, Zhujiang Newtown, Tianhe District, Guangzhou, 510623, China;2. Guangzhou AID Cloud Technology, No. 68 Huacheng Avenue, Tianhe District, Guangzhou, China;3. Cardiac Intensive Care Unit, The Heart Center, Guangzhou Women and Children Medical Center, Guangzhou Medical University, No.9 Jinsui Road, Zhujiang Newtown, Tianhe District, Guangzhou 510623, China;4. Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China;5. Department of Pediatric Surgery, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children''s Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China;1. Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China;2. Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China;3. Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, China;4. Cancer Center of the First Hospital of Jilin University, Changchun, Jilin, 130021, China;5. Department of Gastrointestinal Surgery and Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China;1. Biostatistics and Bioinformatics (DIM), University Hospital, Dijon, France;2. Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany;3. Nîmes Academic Hospital (CHU), Nîmes, France;4. University of Paris, Faculty of Health, Medicine School, Paris, France;5. GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, CMME, Paris, France;6. McGill Group for Suicide Studies, McGill University, Montréal, Canada;7. Moods Team, INSERM UMR-1178, CESP, Le Kremlin-Bicêtre, France;8. Centre de Recherche INSERM Unité 866, Univ. Bourgogne Franche-Comté, F-21000, Dijon, France;9. Services de diabétologie et endocrinologie, CHRU Dijon, Dijon, F-21000, France;10. Inserm, CIC 1432, Dijon, France;11. Dijon University Hospital, Clinical Investigation Center, Clinical Epidemiology/ Clinical Trials Unit, Dijon, France;12. Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, High-Dimensional Biostatistics for Drug Safety and Genomics, CESP, Villejuif, France;1. Nutrition Post-Graduate Program, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil;2. Department of Clinical Analysis, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil;1. Servicio de Endocrinología y Nutrición, Hospital Regional Universitario de Málaga, Málaga, Spain;2. Servicio de Endocrinología y Nutrición, Hospital Universitario San Cecilio de Granada, Granada, Spain;3. Instituto de Investigación Biosanitaria de Granada (Ibs. Granada), Granada, Spain;4. Servicio de Documentación y Archivo de Historias Clínicas Del Hospital Regional Universitario de Málaga, Málaga, Spain;5. Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain;6. Universidad de Málaga, Málaga, Spain;7. Servicio de Oncología Médica, Hospital Regional Universitario de Málaga, Málaga, Spain;8. Servicio de Hematología y Hemoterapia, Hospital Regional Universitario de Málaga, Málaga, Spain;9. CIBERDEM (Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas), Instituto de Salud Carlos III, Madrid, Spain
Abstract:
Keywords:Malnutrition  Congenital heart disease  Machine learning  Children  Interpretability
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