The model is trained on the first 6, 12, and 24 h of data and then used to predict the ventilation and mortality rates for the next 48 h as shown in Fig. 5. The features extracted from the data include various physiological parameters such as heart rate, blood pressure, respiratory ...
1c). During the training, we optimize the network parameters by alternately training between the joint group identification and cross-modal prediction tasks, which are linked in the shared latent space (Fig. 1d, e). In addition, as a trained UnitedNet combines information for both multimodal ...
Furthermore, a predict-then-optimize (PTO) control strategy is employed, where an optimization algorithm iteratively refines control parameters based on the MTL model's predictions. The mean error in the prediction of airflow for this system is only 1.8%, while the mean error in differential ...
Single-task model.For all experiments, the selected single-task model used to predict each clinical outcome is an MLP (see Methods, sectionModels), as MLPs show a substantial and consistent increase in performance compared to LRs for all tasks (see Supplementary Table24, section A). HCRs extra...
The reconstruction loss \({\mathcal {L}}_\textrm{Rec}\) optimizes the mask conditioned reconstruction module to predict the query related to the given mask. The IVC loss \({\mathcal {L}}_\textrm{IVC}\) enhances the optimization of the mask generation module, aiming to increase the dist...
In Machine Learning, the most common way to address a given problem is to optimize an error measure by training a single model to solve the desired task. However, sometimes it is possible to exploit latent information from other related tasks to improve the performance of the main one, result...
In level 0, one model is trained to predict whether an input sequence is an ARG or not. If it is an ARG, it will go through the second level prediction, in which a multi-task deep learning model (more details shown in the bottom panel) is trained to predict the resistant antibiotic ...
The Gini importance49 of each feature is then determined using a random forest classifier with 10,000 trees implemented with the scikit-learn51 Python library and trained to predict a single task using the 200 extracted radiomic features. The 6 features with the highest Gini importance are ...
This method includes a semi-supervised learning method, which first pre-trains the CNN model to predict a few statistical features in the sampled packets. They use the time-series features of sampled packets. Then, they replace the last few layers with new ones and retrain with a small ...
The optimizer sub-module 26 optimizes the parameters of the Bayesian module while: (1) decomposing the matrix-variate prior to find low-rank matrix encoding task correlations; and (2) inferring prior distributions for the blocks of the multi-variate prior that enforce block sparsity. The ...