Advanced Intelligent Data Analysis for Medical Diagnosis

Dear Colleagues,

Making an accurate diagnosis is a major challenge for global healthcare systems since it requires complicated and coordinated information collection and clinical reasoning operations. On the other hand, developments in information technology, particularly the pervasiveness of mobile technologies adopted in the biomedical and health sciences, have generated mountains of data about health and wellbeing from a variety of sources, including electronic health records in primary care and secondary care, genome-wide studies, demographics, doctors' notes, clinical images, laboratory results, genetic tests, wearable sensors, etc. Improving diagnostic accuracy needs the co-design and co-creative activities by data scientists and healthcare professionals to develop efficient ways of analyzing such a scale of data from multiple sources.

The purpose of this Special Issue is to investigate how advanced intelligent data analysis techniques, such as machine learning/deep learning, responsible AI (including ethical, trustworthy, interpretable/explainable, transparent AI techniques), natural language processing, data mining, statistical learning, etc., hold their promises to more efficiently analyze data in order to extract useful information and improve clinical decision making and medical diagnosis.

Prof. Dr. Shang-Ming Zhou
Dr. Honghan Wu
Dr. Haider Raza
Guest Editors

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