Big Data is on everyone’s lips, even in healthcare. However, it is not always clear how the scientific data collected can be used in the medical and health sector. The first step is to understand what information can be obtained, thanks to technology, about citizens’ preferences and interests, and then to be able to use it to offer an even more useful health experience and promote research. This is the challenge of using Big Data in medicine, even closer to a promise than to reality.
There are thousands of terabytes of medical and scientific data, distributed by research projects, publications and digital medical records. If analyzed carefully and with the appropriate tools, these can give the right boost to research in the health sector, not only as regards the production of new drugs, but also for the choice of a new clinical treatment. Therefore, a potentially endless database is created, enriched every day through e-Health services, such as the electronic health record, the ESF and the history of appointments, diagnoses and recipes of each patient, which are inserted and archived thanks also medical management software used by healthcare facilities and health professionals.
Among the many information that can be collected about patients, there are also indications of preferences that may seem little close to the health sector, but which reveal new information taken directly from online behaviours. A Google search, the content of an email, shared posts on social media can tell citizens’ concerns and priorities. The most classic example is that of Derrick de Kerckhove, who, in 2012, thanks to the use of Big Data, discovered that people feared the outbreak of a new epidemic rather than terrorism.
Once Big Data is made available to the specialist, pharmacist or healthcare professional, the problem arises of how to use it, which in turn hides a central theme: that of data classification. On the one hand, there are personal data, therefore relating to the patient-citizen, and on the other organizational data which, on the other hand, describe the activity of the body responsible for protecting the health of the citizen, whether it is a facility or a clinic.
The potential of big data in medicine is enormous, and those who have been studying the sector for years are convinced that it can actually innovate and improve healthcare profoundly. The positive fallout will, of course, be on the patient. Knowing more information on his state of health would allow everyone to be followed in an increasingly personal and “tailor-made” way.
According to a study published in Technology Review, for example, thanks to big data it is possible to collect enough data to allow doctors to know 12 months in advance and with a 98% safety percentage if a specific drug will cause side effects due to a particular patient. Information that can be used to prescribe a different medicine, helping the patient to heal earlier, but also to save money.
Careful use of Big Data also allows the citizen to be made responsible for his health because people can authorize the sharing of some daily information such as the rhythm of sleep or the heartbeat. Any anomaly is, therefore, immediately detected and treated.
This special issue aims at providing a platform to present recent advancements in the convergent research about big data for medicine. We invite original research papers that report on state-of-the-art and recent achievements in carrying out research works involving big data for medicine.
Overall, we are interested in receiving papers related to the following topics which include but are not limited to:
Algorithms for Natural language processing and clinical pattern recognitions
Behavioral, Environmental, and Public Health Informatics
Biological Network Modeling and Analysis
Biomedical Imaging and Data Visualization
Clinical and Health Decision Support Systems
Content-based image retrieval
Data breach prevention and security
Data inference, mining, and trend analysis
Healthcare communication networks
Health information systems and convergence of health
Healthcare modeling and simulation
Knowledge discovery and decision support
Mobile Health and Sensor Networks
Predictive Modeling and Analytics in Healthcare
Public health management solutions
Sensor based mHealth apps
Text Mining of Biomedical Literature and Clinical Notes
The submission system will be open around one week before the first paper comes in. When submitting your manuscript please select the article type “VSI: BD4MH”. Please submit your manuscript before the submission deadline.
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Paper submission due date: October 30, 2020
Notification of acceptance: December 30, 2020
Revised version due date: January 30, 2021
Camera-ready copy due date: February 30, 2021
Expected publication in Big Data Research special issue: 2021
University of Naples Federico II, Italy
Edge-Hill University, UK
Incheon University, South Korea
Consorzio Interuniversitario Nazionale per l’Informatica
Research Laboratory ITEM-SAVY