Special Issue "2nd Edition of Big Data, Decision Models, and Public Health"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Dr. Chien-Lung Chan
Website
Guest Editor
Dean, Department of Information Management, Yuan Ze University, Taoyuan City, Taiwan
Interests: medical informatics; decision science; big data analytics; public health
Special Issues and Collections in MDPI journals
Prof. Dr. Chi-Chang Chang
Website
Guest Editor
Chair of Medical Informatics Department, Chung Shan Medical University, Taichung City, Taiwan
Interests: medical informatics; clinical decision analysis; simulation modeling; shared medical decision making
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the digital era, the volume and velocity of environmental, population, and public health data from a diverse range of sources are growing rapidly. Big data analytic techniques such as statistical analysis, data mining, machine learning, and deep learning can be applied to construct innovative decision models. Decision-making based on concrete evidence is critical, and has a substantial impact on public health and program implementation. This fact highlights the important role of decision models under uncertainty, including disease control, health intervention, preventive medicine, health services and systems, health disparities and inequalities, quality of life, etc. With complex decision-making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision.

After the success of the previous Special Issue on “Big Data, Decision Models, and Public Health”, we are pleased to invite researchers to contribute to the second Special Issue. Similarly, the aim of this second Special Issue is to collect a series of articles related to big data analytics and forms of public health decision-making based on the decision model, spanning from theory to practice. While working with people’s health and medical information, we also need to commit to scientific integrity issues including people’s privacy, data sharing, bias and uncertainty, research design, and statistical inference. Practical experiences and experiments concerning the above issues in big data analytics are also welcome.

Prof. Dr. Chien-Lung Chan
Prof. Dr. Chi-Chang Chang
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 papers will be 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 semimonthly 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 2300 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.

Keywords

  • Big data analytics
  • Data mining, deep learning, and artificial intelligence
  • Survival analysis and health hazard evaluations
  • Statistics and quality of health/medical big data
  • Intelligent decision-making models in public health
  • Health risk evaluation and modelling
  • Patient safety and outcomes
  • Data-driven decision models with empirical studies
  • Cloud computing and innovative services
  • Decision applications in clinical issues
  • Decision support in traditional Chinese medicine
  • Precision health decision support technologies

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Impact of Matrix Metalloproteinase-11 Gene Polymorphisms on Biochemical Recurrence and Clinicopathological Characteristics of Prostate Cancer
Int. J. Environ. Res. Public Health 2020, 17(22), 8603; https://doi.org/10.3390/ijerph17228603 - 19 Nov 2020
Abstract
Prostate cancer is among the most common malignant tumors worldwide. Matrix metalloproteinase (MMP)-11 is involved in extracellular matrix degradation and remodeling and plays an essential role in cancer development and metastasis. This study investigated the association of MMP-11 polymorphisms with the clinicopathological characteristics [...] Read more.
Prostate cancer is among the most common malignant tumors worldwide. Matrix metalloproteinase (MMP)-11 is involved in extracellular matrix degradation and remodeling and plays an essential role in cancer development and metastasis. This study investigated the association of MMP-11 polymorphisms with the clinicopathological characteristics and biochemical recurrence of prostate cancer. Five single-nucleotide polymorphisms (SNPs) of the MMP-11 were analyzed in 578 patients with prostate cancer through real-time polymerase chain reaction analysis. A prostate-specific antigen level of >10 ng/mL, Gleason grade groups 4 + 5, advanced tumor stage, lymph node metastasis, invasion, and high-risk D’Amico classification were significantly associated with biochemical recurrence in the patients (p < 0.001). MMP-11 rs131451 “TC + CC” polymorphic variants were associated with advanced clinical stage (T stage; p = 0.007) and high-risk D’Amico classification (p = 0.015) in patients with biochemical recurrence. These findings demonstrate that MMP-11 polymorphisms were not associated with prostate cancer susceptibility; however, the rs131451 polymorphic variant was associated with late-stage tumors and high-risk D’Amico classification in prostate cancer patients with biochemical recurrence. Thus, the MMP-11 SNP rs131451 may contribute to the tumor development in prostate cancer patients with biochemical recurrence. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
Show Figures

Figure 1

Back to TopTop