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Found 5 papers, 4 papers with code
SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction
The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain.
A Bayesian Optimization approach for calibrating large-scale activity-based transport models
The use of Agent-Based and Activity-Based modeling in transportation is rising due to the capability of addressing complex applications such as disruptive trends (e. g., remote working and automation) or the design and assessment of disaggregated management strategies.
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction
Thus, the federated approach can be seen as a valid and privacy-preserving alternative to centralized machine learning for classifying ICU mortality when sharing sensitive patient data between hospitals is not possible.
Multi-label Methods for Prediction with Sequential Data
In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data.
Multi-label Classification using Labels as Hidden Nodes
We extend some recent discussion in the literature and provide a deeper analysis, namely, developing the view that label dependence is often introduced by an inadequate base classifier, rather than being inherent to the data or underlying concept; showing how even an exhaustive analysis of label dependence may not lead to an optimal classification structure.
