Applications of Big Data Mining in Healthcare

Dear Colleagues,

Rapidly growing healthcare data have encompassed multiple scales ranging from individuals to populations and have connected multiple sources of the healthcare system, such as clinical records, guidelines, and patient-provided data obtained during diagnosis, prognosis, treatment and postoperative follow-up. These data are becoming a valuable resource for improving clinical activity with technology and bridging the gap between AI research and clinical applications. Meanwhile, the extraction and understanding of vast amounts of large, noisy and complex medical data create substantial barriers to the translation of routinely collected data into knowledge that drives the continual improvement of clinical solutions.

For medical informatics needs, AI technologies including computer vision, natural language processing, machine learning, data mining, and knowledge representation have recently made significant advances in processing medical and healthcare vision or text data and enabling decision makers to better interpret and utilize data. In particular, deep learning often results in automated feature discovery from data, and it has demonstrated impressive performance compared to traditional learning methods when processing large unstructured corpora.

This Special Issue aims at leveraging AI to improve healthcare delivery and management, and to promote fruitful collaborations between different disciplines, including applied mathematics, computer science, medical informatics, computational biology, clinical medicine, and healthcare research.

We invite submission of high-quality manuscripts reporting relevant research in the area of collecting, learning, mining, and understanding various bioinformatics data. Topics of interest include, but are not limited to:

• Knowledge acquisition, cleaning and assembly from structured and unstructured biomedical data silos;
• Knowledge analysis and discovery from large, temporal and uncertainty-ridden healthcare data;
• Medical data query processing and computing;
• Multimodal knowledge representation and fusion;
• Biomedical data management;
• Healthcare planning with big data evaluation and assessment;
• Data mining and machine learning algorithms for clinical solutions;
• Data-driven medical decision support system design;
• Electronic medical record data security and privacy.

Dr. Dagang Li
Dr. Ying Shen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• big data
• data mining
• machine learning
• healthcare
• health assessment
• disease treatment
• disease prevention
• infectious diseases
• chronic diseases
• epidemiology
• mental health