Medical Data Mining: Advances towards Widespread Applications

Dear Colleagues,

Data mining is the process of searching large-scale data to find patterns, anomalies and correlations, which are used for predicting outcomes. In particular, finding patterns in massive amounts of unexplored data requires the use of mathematics and statistics, which presents a unique challenge for researchers and industries. For example, medical data mining is used in healthcare to extract valuable patient information and compare the symptoms of multiple patients being treated for the same condition. This information could be used to help medical staff optimize treatment plans, improve clinical decision-making, increase diagnosis accuracy and improve treatment efficiency. The key to designing concrete medical data mining solutions is the exploitation of technologies such as advanced statistical technologies, artificial intelligence (AI), predictive analysis, data acquisition technologies (e.g., medical internet-of-things (MIoT) and wearable devices), data preprocessing techniques and interpretation using explainable AI  (XAI) tools.

Although there has been considerable progress toward medical data mining, more research innovation, dissemination and technologies are needed to move towards applying medical data mining in smart healthcare applications. This Special Issue aims to explore new research directions in medical data mining towards smart healthcare applications. To this end, we will present the current state-of-the-art and identify future research directions in medical data mining. Additionally, this Special Issue is designed to highlight the applications, industrial experiments, studies, datasets and use cases of medical data mining-based applications.     

The suggested topics of interest include, but are not limited to:

• Medical data mining-based frameworks and solutions for smart healthcare applications;
• Modelling for medical data mining;
• Mathematical and statistical methods for data mining;
• Multi-modal medical data mining;
• Data privacy, ethics and regulations for medical data mining and smart healthcare;  
• Medical data interpretability using XAI;
• Medical IoT technologies for data acquisition in medical data mining; 
• Machine learning and deep learning models for medical data mining-based application; 
• Distributed machine learning/federated learning/ensemble learning for medical data mining-based applications;
• Medical data mining-based blockchain technologies for smart healthcare;
• Future digital twin/human digital twin applications based on medical data mining;
• Decision-making applications based on medical data mining;
• Medical data mining and Healthcare 4.0/5.0 applications;
• Medical data mining and the COVID-19 pandemic;
• Blockchain and decentralization for medical data mining.

Dr. Radhya Sahal
Guest Editor

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• medical data mining
• smart healthcare
• machine learning
• deep learning
• federated learning
• ensemble learning
• explainable AI
• medical IoT
• COVID-19
• mathematics
• statistics
• blockchain
• personal digital twins
• Healthcare 4.0/5.0