An interpretable mortality prediction model for COVID-19 patients
By Li Yan, Hai-Tao Zhang, Jorge Gonçalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, L. Jing, Mingyang Zhang and 20 more. Nature Machine Intelligence, 2020
892 citations recorded by the source corpus.
Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/bdef059783dd682dbf79b2ae0c1fad470515686c
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Citations in the corpus
- Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “The sum of the transition probabilities in each row is equal to one, which is mathematically shown as follows [33] : Since the transition for the last m entries of the series is indefin-able, M i should be counted by the first as n − m entries; n is the value of entries of the original series.”
- Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review (2020, positive): “…three significant key features (high-sensitivity C-reactive protein, lymphocyte and lactic dehydrogenase (LDH)) of the 75 features clinical and blood test samples result to be the highest rank of 90% accuracy in predicting and assessing Covid-19 patient into general, severe and mortality rate [39] .”
- Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis (2020, background): “There is no currently available prognostic biomarker to identify patients requiring immediate medical attention and to estimate their associated mortality rate [19].”