Titlebook: Artificial Intelligence in Medicine; 21st International C Jose M. Juarez,Mar Marcos,Allan Tucker Conference proceedings 2023 The Editor(s)

ergonomics

書目名稱Artificial Intelligence in Medicine影響因子(影響力)




書目名稱Artificial Intelligence in Medicine影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence in Medicine網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence in Medicine網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence in Medicine被引頻次




書目名稱Artificial Intelligence in Medicine被引頻次學(xué)科排名




書目名稱Artificial Intelligence in Medicine年度引用




書目名稱Artificial Intelligence in Medicine年度引用學(xué)科排名




書目名稱Artificial Intelligence in Medicine讀者反饋




書目名稱Artificial Intelligence in Medicine讀者反饋學(xué)科排名

粗糙濫制

建筑師

Gary R. Hudes MD,Jessie Schol RNsicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.

orient

Geistesgeschichtliche Faschismusdiagnosen,nd new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.

Ferritin

Bombast

deficiency

Causal Discovery with?Missing Data in?a?Multicentric Clinical Studysicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.

MAUVE

Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detectionnd new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.

indigenous

Computational Evaluation of?Model-Agnostic Explainable AI Using Local Feature Importance in?Healthcaocal feature importances) as features and the output of the prediction problem (labels) again as labels. We evaluate the method based a real-world tabular electronic health records dataset. At the end, we answer the research question: “How can we computationally evaluate XAI Models for a specific prediction model and dataset?”.

Focus-Words

Batch Integrated Gradients: Explanations for?Temporal Electronic Health RecordsRecords (EHRs), we see patient records can be stored in temporal sequences. Thus, we demonstrate Batch-Integrated Gradients in producing explanations over a temporal sequence that satisfy proposed properties corresponding to XAI for EHR data.