Medical Information Processing
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 10290
Special Issue Editors
Interests: data mining; data science project development; medical data analysis; NLP in medical domain
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; data mining; data stream mining; machine learning; random forests
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear colleagues,
The abundance of medical data coupled with the advances in machine learning algorithms have led to notable progress in the area of medical information processing over the past decade. A persistent challenge in medical data processing is the uncertainty of machine learning models that negatively affects their trustworthiness. Despite the continuing progress of machine learning accuracy in the medical domain, trust in these models has been an issue. This is especially true with the significant leaps in progress when adopting deep learning in the medical domain because of the black box nature of the produced models. This Special Issue invites contributions from both academia and the industry to report on their results using different machine learning and theoretical information methods applied in the medical domain. Topics addressing the uncertainty of machine learning methods and explainability of black box models are especially welcome although we also welcome contributions from the wider domain of medical information processing.
Dr. Ernestina Menasalvas
Prof. Dr. Mohamed Medhat Gaber
Guest Editors
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Keywords
- Deep learning methods on medical data
- Uncertainty quantification of machine learning models
- Information theory in machine learning applied in the medical domain
- Explainability of machine learning models in the medical domain
- Applications of information theory in medical data processing
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