Predictive Modeling of COVID-19 Patient Recovery Using Complete Blood Count Data
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Publisher
Springer Nature Singapore
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https://link.springer.com/content/pdf/10.1007/978-981-97-3191-6_4
Reference17 articles.
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2. Rahman, T., Khandakar, A., Hoque, M.E., Ibtehaz, N., Kashem, S.B., Masud, R., et al.: Development and validation of an early scoring system for prediction of disease severity in COVID-19 using complete blood count parameters. IEEE Access 9, 120422–120441 (2021)
3. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436-444 (2015)
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5. Setio, A.A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S.J., Wille, M.M., Naqibullah, M., Sanchez, C.I., van Ginneken, B.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160–1169 (2016). https://doi.org/10.1109/TMI.2016.2536809. Epub 1 Mar 2016. PMID: 26955024
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