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

hermetic

Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detectionrowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windows and ML models, a feature impact analysis using Shapley Additive Explanations (SHAP), an

Champion

Computational Evaluation of Model-Agnostic Explainable AI Using Local Feature Importance in Healthcaased medical practice. In the XAI field, effective evaluation methods are still being developed. The straightforward way is to evaluate via user feedback. However, this needs big efforts (applying on high number of users and test cases) and can still include various biases inside. A computational ev

OPINE

Batch Integrated Gradients: Explanations for Temporal Electronic Health Recordscerned with analysing temporal data. Namely, we must consider a sequence of instances that occur in time, and explain why the prediction transitions from one time point to the next. Currently, XAI techniques do not leverage the temporal nature of data and instead treat each instance independently. T

fatty-acids

Improving Stroke Trace Classification Explainability Through Counterexamples are typically not explainable, an issue which is particularly relevant in medicine..In our recent work we tackled this problem, by proposing ., a novel tool able to highlight what trace activities are particularly significant for the classification task. A trace saliency map is built by generating

Pedagogy

Ebct207

Artificial Intelligence in Medicine978-3-031-34344-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

协奏曲

Amita Patnaik MD,Eric K. Rowinsky MD clinicians to understand it. In this paper, we approach this problem by defining the technical task of mining diverse top-k phenotypes and proposing an algorithm called DSLM to solve it. The phenotypes obtained are evaluated according to their quality and predictive capacity in a bacterial infection problem.

acheon

A Return to Halves in the Twentieth Century,lized therapeutic approaches. To address this issue, clustering algorithms are often employed that identify patient groups with homogeneous characteristics. Clustering algorithms are mainly unsupervised, resulting in clusters that are biologically meaningful, but not necessarily correlated with a cl

炸坏

–FER

Farming in Norfolk Around 1800, prevent disease worsening, but selecting the right treatment is difficult due to the heterogeneity. To alleviate this decision-making process, predictions of the long-term prognosis of the individual patient are of interest (especially at diagnosis, when not much is known yet). However, most progno