A processor of a system trains one or more neural networks to predict a treatment for a patient based, at least in part, on medical imaging data and clinical metadata, and determines a treatment for the patient using the trained neural net. Whether the patient is part of a patient population to receive a treatment is determined. The medical imaging data may comprise slices of a computer tomography scan. The clinical metadata may comprise laboratory measurements of lactate dehydrogenase levels and C-reactive protein levels. The treatment may be for COVID-19. An aggregate image-based treatment probability for a plurality of images may be determined. The aggregate probability and clinical metadata may be normalised to obtain input features used to train a portion of the neural networks to obtain weights indicating how impactful each feature is to determining the treatment. A multi-modal deep learning framework and the EfficientNet convolutional neural network may be used.
Machine-learning techniques for oxygen therapy prediction using medical imaging data and clinical metadata