Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia
Background The time after hospital discharge carries high rates of mortality in neonates and young children in sub-Saharan Africa. Previous work using logistic regression to develop risk assessment tools to identify those at risk for postdischarge mortality has yielded fair discriminatory value. Our...
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BMJ Publishing Group
2025-06-01
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| Series: | BMJ Paediatrics Open |
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| author | Rodrick Kisenge Todd A. Florin Cynthia G. Whitney Michelle Niescierenko Julia Kamara Abraham Samma Evance Godfrey Chris A. Rees Readon C. Ideh Ye-Jeung G. Coleman-Nekar Hussein K. Manji Christopher R. Sudfeld Adrianna L. Westbrook Claudia R. Morris Karim P. Manji Christopher P. Duggan Rishikesan Kamaleswaran |
| author_facet | Rodrick Kisenge Todd A. Florin Cynthia G. Whitney Michelle Niescierenko Julia Kamara Abraham Samma Evance Godfrey Chris A. Rees Readon C. Ideh Ye-Jeung G. Coleman-Nekar Hussein K. Manji Christopher R. Sudfeld Adrianna L. Westbrook Claudia R. Morris Karim P. Manji Christopher P. Duggan Rishikesan Kamaleswaran |
| author_sort | Rodrick Kisenge |
| collection | DOAJ |
| description | Background The time after hospital discharge carries high rates of mortality in neonates and young children in sub-Saharan Africa. Previous work using logistic regression to develop risk assessment tools to identify those at risk for postdischarge mortality has yielded fair discriminatory value. Our objective was to determine if machine learning models would have greater discriminatory value to identify neonates and young children at risk for postdischarge mortality.Methods We conducted a planned secondary analysis of a prospective observational cohort at Muhimbili National Hospital in Dar es Salaam, Tanzania and John F. Kennedy Medical Center in Monrovia, Liberia. We enrolled neonates and young children near the time of discharge. The outcome was 60-day postdischarge mortality. We collected socioeconomic, demographic, clinical, and anthropometric data during hospital admission and used machine learning (ie, eXtreme Gradient Boosting (XGBoost), Hist-Gradient Boost, Support Vector Machine, Neural Network, and Random Forest) to develop risk assessment tools to identify: (1) neonates and (2) young children at risk for postdischarge mortality.Results A total of 2310 neonates and 1933 young children enrolled. Of these, 71 (3.1%) neonates and 67 (3.5%) young children died after hospital discharge. XGBoost, Hist Gradient Boost, and Neural Network models yielded the greatest discriminatory value (area under the receiver operating characteristic curves range: 0.94–0.99) and fewest features, which included six features for neonates and five for young children. Discharge against medical advice, low birth weight, and supplemental oxygen requirement during hospitalisation were predictive of postdischarge mortality in neonates. For young children, discharge against medical advice, pallor, and chronic medical problems were predictive of postdischarge mortality.Conclusions Our parsimonious machine learning-based models had excellent discriminatory value to predict postdischarge mortality among neonates and young children. External validation of these tools is warranted to assist in the design of interventions to reduce postdischarge mortality in these vulnerable populations. |
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| publishDate | 2025-06-01 |
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| spelling | doaj-art-c9cfbd3b5e7a4b43812afe4ad7f8d5fa2025-08-20T02:37:09ZengBMJ Publishing GroupBMJ Paediatrics Open2399-97722025-06-019110.1136/bmjpo-2025-003547Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, LiberiaRodrick Kisenge0Todd A. Florin1Cynthia G. Whitney2Michelle Niescierenko3Julia Kamara4Abraham Samma5Evance Godfrey6Chris A. Rees7Readon C. Ideh8Ye-Jeung G. Coleman-Nekar9Hussein K. Manji10Christopher R. Sudfeld11Adrianna L. Westbrook12Claudia R. Morris13Karim P. Manji14Christopher P. Duggan15Rishikesan Kamaleswaran16Department of Paediatrics and Child Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of TanzaniaDivision of Emergency Medicine, Ann and Robert H. Lurie Childrens Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USAFrom the Department of Medicine (D.M.T., S.M.R., T.A.J.), Emory University School of Medicine; The Office of Health Promotion and Disease Prevention (F.J.M., T.A.J.), Grady Health System; The Atlanta Veterans Affairs Medical Center (J.S.D., G.O.) and the Georgia Emerging Infections Program (D.M.T., S.M.R.,J.S.D., G.O.); Centers for Disease Control and Prevention (C.G.W.), Atlanta, GA.Harvard Humanitarian Initiative, Cambridge, Massachusetts, USADepartment of Pediatrics, John F. Kennedy Medical Center, Monrovia, LiberiaDepartment of Paediatrics and Child Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of TanzaniaDepartment of Paediatrics and Child Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of TanzaniaDivision of Pediatric Emergency Medicine, Emory University School of Medicine, Atlanta, Georgia, USADepartment of Pediatrics, John F. Kennedy Medical Center, Monrovia, LiberiaDepartment of Pediatrics, John F. Kennedy Medical Center, Monrovia, LiberiaAccident and Emergency Department, Aga Khan Health Services, Dar es Salaam, TanzaniaDepartments of Nutrition and Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USAPediatrics Biostatistics Core, Department of Pediatrics, Emory University, Atlanta, Georgia, USADivision of Pediatric Emergency Medicine, Emory University School of Medicine, Atlanta, Georgia, USADepartment of Pediatrics and Child Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, United Republic of TanzaniaDepartments of Nutrition and Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USADivision of Translational Biomedical Informatics, Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USABackground The time after hospital discharge carries high rates of mortality in neonates and young children in sub-Saharan Africa. Previous work using logistic regression to develop risk assessment tools to identify those at risk for postdischarge mortality has yielded fair discriminatory value. Our objective was to determine if machine learning models would have greater discriminatory value to identify neonates and young children at risk for postdischarge mortality.Methods We conducted a planned secondary analysis of a prospective observational cohort at Muhimbili National Hospital in Dar es Salaam, Tanzania and John F. Kennedy Medical Center in Monrovia, Liberia. We enrolled neonates and young children near the time of discharge. The outcome was 60-day postdischarge mortality. We collected socioeconomic, demographic, clinical, and anthropometric data during hospital admission and used machine learning (ie, eXtreme Gradient Boosting (XGBoost), Hist-Gradient Boost, Support Vector Machine, Neural Network, and Random Forest) to develop risk assessment tools to identify: (1) neonates and (2) young children at risk for postdischarge mortality.Results A total of 2310 neonates and 1933 young children enrolled. Of these, 71 (3.1%) neonates and 67 (3.5%) young children died after hospital discharge. XGBoost, Hist Gradient Boost, and Neural Network models yielded the greatest discriminatory value (area under the receiver operating characteristic curves range: 0.94–0.99) and fewest features, which included six features for neonates and five for young children. Discharge against medical advice, low birth weight, and supplemental oxygen requirement during hospitalisation were predictive of postdischarge mortality in neonates. For young children, discharge against medical advice, pallor, and chronic medical problems were predictive of postdischarge mortality.Conclusions Our parsimonious machine learning-based models had excellent discriminatory value to predict postdischarge mortality among neonates and young children. External validation of these tools is warranted to assist in the design of interventions to reduce postdischarge mortality in these vulnerable populations.https://bmjpaedsopen.bmj.com/content/9/1/e003547.full |
| spellingShingle | Rodrick Kisenge Todd A. Florin Cynthia G. Whitney Michelle Niescierenko Julia Kamara Abraham Samma Evance Godfrey Chris A. Rees Readon C. Ideh Ye-Jeung G. Coleman-Nekar Hussein K. Manji Christopher R. Sudfeld Adrianna L. Westbrook Claudia R. Morris Karim P. Manji Christopher P. Duggan Rishikesan Kamaleswaran Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia BMJ Paediatrics Open |
| title | Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia |
| title_full | Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia |
| title_fullStr | Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia |
| title_full_unstemmed | Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia |
| title_short | Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia |
| title_sort | machine learning approaches to identify neonates and young children at risk for postdischarge mortality in dar es salaam tanzania and monrovia liberia |
| url | https://bmjpaedsopen.bmj.com/content/9/1/e003547.full |
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