Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients
Author:
Publisher
AME Publishing Company
Subject
General Medicine
Link
https://atm.amegroups.com/article/download/91455/pdf
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi;Computational and Structural Biotechnology Journal;2024-12
2. MI-DenseCFNet: deep learning–based multimodal diagnosis models for Aureus and Aspergillus pneumonia;European Radiology;2024-01-17
3. Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte;Journal of Translational Medicine;2023-07-12
4. Evaluating the efficiency of a nomogram based on the data of neurosurgical intensive care unit patients to predict pulmonary infection of multidrug-resistant Acinetobacter baumannii;Frontiers in Cellular and Infection Microbiology;2023-04-25
5. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders;Open Life Sciences;2023-01-01
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