An AI-Based Model for the Prediction of a Newborn’s Sickle Cell Disease Status
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-23116-2_7
Reference13 articles.
1. Thiam, L., et al.: Profils épidemiologiques, cliniques et hématologiques de la drépanocytose homozygote SS en phase inter critique chez l’enfant à Ziguinchor, Sénégal. Pan Afr. Med. J. 28, 208 (2017). https://doi.org/10.11604/pamj.2017.28.208.14006
2. Milton, J.N., Gordeuk, V.R., Taylor, J.G., Gladwin, M.T., Steinberg, M.H., Sebastiani, P.: Prediction of fetal hemoglobin in sickle cell anemia using an ensemble of genetic risk prediction models. Circ. Cardiovasc. Genet. 7, 110–115 (2014). https://doi.org/10.1161/CIRCGENETICS.113.000387
3. Alharbi, N.H., Bameer, R.O., Geddan, S.S., Alharbi, H.M.: Recent advances and machine learning techniques on sickle cell disease. Future Comput. Inform. J. 5, 4(2020). https://doi.org/10.54623/fue.fcij.5.1.4
4. Patel, A., et al.: Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease. Br. J. Haematol. 192, 158–170 (2021). https://doi.org/10.1111/bjh.17107
5. Sen, B., Ganesh, A., Bhan, A., Dixit, S., Goyal, A.: Machine learning based Diagnosis and classification of Sickle Cell Anemia in Human RBC. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). pp. 753–758 (2021). https://doi.org/10.1109/ICICV50876.2021.9388610
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