Manuscript Topics

In the last decades, there has been an increase of the amount of data in the healthcare context. Data can be acquired from electronic medical records that are systematically registered in the hospitals, from sensors that are widely used for measuring several biomedical parameters but also in specialist laboratories such as those used for gait analysis, postural control and in rehabilitation engineering. New techniques are available also for extracting quantitative parameters from images: radiomics processes are often used to make a diagnosis on quantitative data from an image. Starting from analyzing the data of our routine life (there is the clear example of social media and sentiment analysis), the development of mathematical models of health and diseased states along with the availability of novel machine learning and data mining tools have begun to influence also the healthcare context. The use of such techniques is fundamental in order to exploit the great volume of heterogeneous data and help both clinicians and physicians in producing diagnosis and prognosis where they can be harder as well as in examining pathologies with the use of mathematical and engineering approaches (simulation models, algorithms for analysis of biomedical data and signals, etc.). Moreover, there is the possibility to apply these techniques for assessing treatments with an image interpretation or through real-time health monitoring. Finally, the applications in a clinical workflow are available in literature through natural language processing on clinical notes, audio/speech processing and clinical time-series data analysis. Cardiology, neurology, oncology and rehabilitation engineering are a few examples of fields where several researchers have tried to apply these techniques successfully.

This Special Issue aims to publish original and innovative researches related to the implementation of mathematical models, machine learning and data mining algorithms, but also to the use of state-of-art algorithms, to solve biomedical issues and model health and diseases. Review articles, which summarize the state of the art and recent advances of this topic in the different medical specialties, are also welcome.

Potential topics include but are not limited to the following:
Exploiting electronic medical records to produce a diagnosis through data mining techniques and mathematical models;
Simulation and mathematical models of health and disease;
Analysis and processing of medical images;
Producing prognosis to help clinicians and physicians through machine learning and mathematical models;
Techniques to tackle the issue of unbalanced biomedical datasets;
Radiomics and machine learning in healthcare;
Speech and audio processing to aid clinical decision making;
Extraction of parameters from biomedical signals to perform classification analyses;
Machine learning and mathematical models to evaluate medical treatments.

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