60 citations · 78 across the 5 of their papers we have counts for
5 papers
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment
Hao Tang, Aref Miri Rekavandi, Dharjinder Rooprai +4
This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviour…
3D Brain and Heart Volume Generative Models: A Survey
Yanbin Liu, Girish Dwivedi, Farid Boussaid +1
Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capabil…
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation
Juan Lu, Rebecca Hutchens, Joseph Hung +9
Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of ischemic stroke and bleeding risks. However, risk stratification schemas such a…
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?
Isaac Ronald Ward, Ling Wang, Juan lu +3
Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) mode…
Imputation of Missing Data with Class Imbalance using Conditional Generative Adversarial Networks
Saqib Ejaz Awan, Mohammed Bennamoun, Ferdous Sohel +2
Missing data is a common problem faced with real-world datasets. Imputation is a widely used technique to estimate the missing data. State-of-the-art imputation approaches, such as…