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tegies for improving more efficient use of MAUs, and the impact this has on clinical outcomes, length of stay, and patient flow measures in MAUs. Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets. RNA seque
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