|
[1]
|
WHO (2020) The Top 10 Causes of Fudan University Death.
https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
|
|
[2]
|
WHO (2021) World Patient Safety Day 2021.
https://www.who.int/news-room/events/detail/2021/09/17/default-calendar/world-patient-safety-day-2021
|
|
[3]
|
McOmber, B.G., Moreira, A.G., Kirkman, K., Acosta, S., Rusin, C. and Shivanna, B. (2024) Predictive Analytics in Bronchopulmonary Dysplasia: Past, Present, and Future. Frontiers in Pediatrics, 12, Article 1483940.
https://doi.org/10.3389/fped.2024.1483940
|
|
[4]
|
姜齐, 张莉. 人工智能在新生儿坏死性小肠结肠炎 辅助诊疗中的研究进展[J]. 中国当代儿科杂志, 2025, 27(10): 1281-1285.
|
|
[5]
|
Hulsen, T., Jamuar, S.S., Moody, A.R., Karnes, J.H., Varga, O., Hedensted, S., et al. (2019) From Big Data to Precision Medicine. Frontiers in Medicine, 6, Article 34.
https://doi.org/10.3389/fmed.2019.00034
|
|
[6]
|
Nielsen, A.N. and Smyser, C.D. (2025) Machine Learning for Outcome Prediction in Preterm Infants: Opportunities and Challenges. Pediatric Research, 98, 759-761.
https://doi.org/10.1038/s41390-025-04131-9
|
|
[7]
|
Pablo, R.J., Roberto, D., Victor, S., Isabel, G., Paul, C. and Elizabeth, O. (2022) Big Data in the Healthcare System: A Synergy with Artificial Intelligence and Blockchain Technology. Journal of Integrative Bioinformatics, 19, Article ID: 20200035.
https://doi.org/10.1515/jib-2020-0035
|
|
[8]
|
Shilo, S., Rossman, H. and Segal, E. (2020) Axes of a Revolution: Challenges and Promises of Big Data in Healthcare. Nature Medicine, 26, 29-38.
https://doi.org/10.1038/s41591-019-0727-5
|
|
[9]
|
Teji, J.S., Jain, S., Gupta, S.K. and Suri, J.S. (2022) NeoAI 1.0: Machine Learning-Based Paradigm for Prediction of Neonatal and Infant Risk of Death. Computers in Biology and Medicine, 147, Article ID: 105639.
https://doi.org/10.1016/j.compbiomed.2022.105639
|
|
[10]
|
Zhang, Z., Xiao, Q. and Luo, J. (2023) Infant Death Prediction Using Machine Learning: A Population-Based Retrospective Study. Computers in Biology and Medicine, 165, Article ID: 107423.
https://doi.org/10.1016/j.compbiomed.2023.107423
|
|
[11]
|
Chang, A.L., Reiss, J.D., Culos, A., Becker, M., Mayo, J.A., Marić, I., et al. (2026) Quantitative Assessment of Neonatal Health Using Dried Blood Spot Metabolite Profiles and Deep Learning. Science Translational Medicine, 18, eadv4942.
https://doi.org/10.1126/scitranslmed.adv4942
|
|
[12]
|
Yang, C., Shi, R., Yuan, J., Ding, Y., Geng, H., Li, H., et al. (2026) Hypertensive Disorders of Pregnancy Contribute to Developmental and Metabolic Multimorbidity in Preterm Infants. Nature Communications, 17, Article No. 8855.
https://doi.org/10.1038/s41467-026-75869-x
|
|
[13]
|
Karaca, C. and Takcı, H.A.M. (2025) Role of Gut Microbiome in Developing Necrotizing Enterocolitis. Folia Microbiologica, 70, 197-204.
https://doi.org/10.1007/s12223-024-01217-5
|
|
[14]
|
Abdellatif, M.A.K., Eyada, E., Rabie, W., Abdelaziz, A. and Shahin, W. (2022) Genetic and Biochemical Predictors of Neonatal Bronchopulmonary Dysplasia. Journal of Pediatric Genetics, 11, 173-178.
https://doi.org/10.1055/s-0040-1721740
|
|
[15]
|
Ahmed, A.E., Ibrahim, F.E., Linga, B.S.G., Alqudah, A.M. and Al-Dewik, N. (2025) Comparative Analysis of Preterm and Full-Term Births Using Transcriptomic Profiling.
https://doi.org/10.57945/manara.32051301
|
|
[16]
|
De Francesco, D., Reiss, J.D., Roger, J., Tang, A.S., Chang, A.L., Becker, M., et al. (2023) Data-Driven Longitudinal Characterization of Neonatal Health and Morbidity. Science Translational Medicine, 15, eadc9854.
https://doi.org/10.1126/scitranslmed.adc9854
|
|
[17]
|
Shu, C., Zebda, R., Espinosa, C., Reiss, J., Debuyserie, A., Reber, K., et al. (2025) Early Prediction of Mortality and Morbidities in VLBW Preterm Neonates Using Machine Learning. Pediatric Research, 97, 2056-2064.
https://doi.org/10.1038/s41390-024-03604-7
|
|
[18]
|
Wagner, M.W., So, D., Guo, T., Erdman, L., Sheng, M., Ufkes, S., et al. (2022) MRI Based Radiomics Enhances Prediction of Neurodevelopmental Outcome in Very Preterm Neonates. Scientific Reports, 12, Article No. 11872.
https://doi.org/10.1038/s41598-022-16066-w
|
|
[19]
|
Luo, H., Li, H., Xiong, S., Wang, R., Li, Z., Wang, J., et al. (2026) Clinical 2D MRI Radiomics for Infant Brain Age and Trajectory Modeling. iScience, 29, Article ID: 116623.
https://doi.org/10.1016/j.isci.2026.116623
|
|
[20]
|
Steiner, M., Urlesberger, B., Giordano, V., Kasprian, G., Glatter, S., Oberleitner-Leeb, C., et al. (2022) Outcome Prediction in Neonatal Hypoxic-Ischaemic Encephalopathy Using Neurophysiology and Neuroimaging. Neonatology, 119, 483-493.
https://doi.org/10.1159/000524751
|
|
[21]
|
Bennis, F.C., Onland, W., van der Vorst, J.P., Hoogendoorn, M., Hutten, G.J., van Kaam, A.H., et al. (2026) Prediction of Bronchopulmonary Dysplasia Seven Days after Birth Using Respiratory and Oxygenation Timeseries with Machine Learning. Pediatric Research.
https://doi.org/10.1038/s41390-026-05301-z
|
|
[22]
|
Huo, H., Lu, Y., Zhou, J., Zhang, L., Pei, J. and Hu, C. (2026) High-Throughput Analysis of Multimodal Monitoring Data: The Role of Machine Learning in Early Warning Systems for High-Risk Neonates. BMJ Paediatrics Open, 10, e004705.
https://doi.org/10.1136/bmjpo-2026-004705
|