Special Issue: Informatics & Data-Driven Medicine-2021
The current direction of development of Medicine today is changing dramatically. Previously, data of the patient’s health were collected only during a visit to the clinic. These were small chunks of information obtained from observations or experimental studies by clinicians, and were recorded on paper or in small electronic files. The advances in computer power development, hardware and software tools and consequently design an emergence of miniature smart devices for various purposes (flexible electronic devices, medical tattoos, stick-on sensors, biochips etc.) can monitor various vital signs of patients in real time and collect such data comprehensively. There is a steady growth of such technologies in various fields of medicine for disease prevention, diagnosis, and therapy.
Due to this, clinicians began to face similar problems as data scientists. They need to perform many different tasks, which are based on a huge amount of data, in some cases with incompleteness and uncertainty and in most others with complex, non-obvious connections between them and different for each individual patient (observation) as well as a lack of time to solve them effectively. These factors significantly decrease the quality of decision making, which usually affects the effectiveness of diagnosis or therapy. That is why the new concept in Medicine, widely known as Data-Driven Medicine, arises nowadays. This approach, which based on IoT and Artificial Intelligence, provide possibilities for efficiently process of the huge amounts of data of various types, stimulates new discoveries and provides the necessary integration and management of such information for enabling precision medical care.
Such approach could create a new wave in health care. It will provide effective management of a huge amount of comprehensive information about the patient's condition; will increase the speed of clinician' expertise, and will maintain high accuracy analysis based on digital tools and machine learning. The combined use of different digital devices and artificial intelligence tools will provide an opportunity to deeply understand the disease, boost the accuracy and speed of its detection at early stages and improve the modes of diagnosis. Such invaluable information stimulates new ways to choose patient-oriented preventions and interventions for each individual case.
This Special Issue is dedicated mainly on the best papers from the 4-rd International Conference on Informatics & Data-Driven Medicine (IDDM-2021) and the 1-st International workshop on Small and Big Data Approaches in Healthcare (SBDaH 2021) that will be held in conjunction with: the 11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2021). Extended versions of these papers, which contain science-intensive solutions based on the recent advance in Informatics, have a strong theoretical basis, as well as demonstrate readiness for practical application in Medicine, will be invited for submission. The IDDM 2021 and SBDaH 2021 Program Committees will recommend it after presentations of all participants during the conference, based on the scientific novelty, practical value and prospects for further research. However, this Special Issue is not limited conference materials. Original papers, related to this Special Issue can also be published.
Big Data and IoT in Medical Applications
Small Data Approach in Medicine
AI-based data augmentation techniques
Ensemble learning for case of Small and Big data processing
Surrogating methods for data augmentation
Statistical learning in precision medicine
Medical and Biomedical Image Processing
Deep Learning Models in Healthcare and Biomedicine
Machine Learning Approaches for Medicine
Bioinformatics for Healthcare Applications
Complex Health Monitoring Systems
Decision fusion for healthcare applications
Semi-supervised learning applied to small data samples
Graph signal processing in big data contexts
IT-enabled Healthcare Services
Analysis and Prediction for Covid'19 Data
Instructions for authors
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