Machine Learning and Biomedical Informatics
Special Issue Editors
Interests: machine learning and data mining; knowledge extraction from medical datasets; feature relevance evaluation; medical dataset processing and management
Interests: machine learning; context-aware inference models; telemedicine applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Artificial intelligence and machine learning are powerful tools for extracting knowledge from the world in a variety of ways. In this Special Issue, we encourage knowledge extraction from biomedical datasets in any way, including the prediction of classes of medical outcomes of any kind or the identification of medical features that are knowledge-related to the class or medical outcome of any kind. This should be accomplished in a way that overcomes the simple classification power of the ML algorithm used. Papers evaluating the assessment of the results of knowledge extraction, i.e., the evaluation of whether the medical feature results are knowledge-related to some medical outcome by medical professionals, are especially welcome. This is an important topic, because the time required for validation of medical knowledge by medical professionals in practice can take years and could be accomplished more quickly using a Delphi methodology or similar methodology. Research on the philosophical aspects and ethical issues of knowledge extraction from biomedical datasets is welcome. For instance, the influence of bias of any kind on the results of machine learning from biomedical datasets and how to discover and prevent it. Papers related to any aspect of the knowledge extraction and mining from biomedical datasets (including big data) are welcome in this Special Issue.
Dr. Ricardo Sánchez De Madariaga
Dr. Juan Martinez-Romo
Guest Editors
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Keywords
- knowledge extraction
- medical feature relevance
- knowledge relevance
- classification power
- bias detection
- knowledge analysis
- machine learning and data mining
- knowledge methodologies
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