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3rd Edition of Big Data, Decision Models, and Public Health
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3rd 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: closed (31 March 2023) | Viewed by 7569

Special Issue Editors

Prof. Dr. Chien-Lung Chan

E-Mail 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, Collections and Topics in MDPI journals
Prof. Dr. Chi-Chang Chang

E-Mail 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, Collections and Topics 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 two Special Issues on “Big Data, Decision Models, and Public Health”, we are pleased to invite researchers to contribute to the third edition of the Special Issue. The aim of this third edition is similar to that of the previous two editions, i.e., 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 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.

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 modeling
  • 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

Related Special Issues

Published Papers (5 papers)

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Research

18 pages, 1141 KiB  
Article
Human Microbiome Mixture Analysis Using Weighted Quantile Sum Regression
by Shoshannah Eggers, Moira Bixby, Stefano Renzetti, Paul Curtin and Chris Gennings
Int. J. Environ. Res. Public Health 2023, 20(1), 94; https://doi.org/10.3390/ijerph20010094 - 21 Dec 2022
Cited by 6 | Viewed by 1288
Abstract
Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted [...] Read more.
Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted quantile sum regression with repeated holdouts (WQSRSRH), a mixture method successfully applied to ‘omic data to account for relationships between many predictors, to processed amplicon sequencing data from the Human Microbiome Project. We simulated a binary variable associated with 20 operational taxonomic units (OTUs). WQSRSRH was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQSRSRH method was also compared to other standard methods for microbiome analysis. The method was further illustrated using real data from the Growth and Obesity Cohort in Chile to assess the association between the gut microbiome and body mass index. In the analysis with simulated data, WQSRSRH predicted the correct directionality of association between the microbiome and the simulated variable, with an average sensitivity and specificity of 75% and 70%, respectively, in identifying the 20 associated OTUs. WQSRSRH performed better than all other comparison methods. In the illustration analysis of the gut microbiome and obesity, the WQSRSRH analysis identified an inverse association between body mass index and the gut microbe mixture, identifying Bacteroides, Clostridium, Prevotella, and Ruminococcus as important genera in the negative association. The application of WQSRSRH to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important to the mixture, and allowing for covariate adjustment. It outperformed other methods when using simulated data, and in analysis with real data found results consistent with other study findings. Full article
(This article belongs to the Special Issue 3rd Edition of Big Data, Decision Models, and Public Health)
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7 pages, 295 KiB  
Article
Analysis of Newborn Hearing Screening Results in South Korea after National Health Insurance Coverage: A Nationwide Population-Based Study
by Kyu Young Choi, Su-Kyoung Park, Sun Choi and Jiwon Chang
Int. J. Environ. Res. Public Health 2022, 19(22), 15052; https://doi.org/10.3390/ijerph192215052 - 16 Nov 2022
Cited by 4 | Viewed by 1145
Abstract
Newborn hearing screening (NHS) has been covered by national health insurance since October 2018 in Korea. However, the results of the NHS are not reported due to the absence of a follow-up tracking system. This study analyzed the status and the predicted referral [...] Read more.
Newborn hearing screening (NHS) has been covered by national health insurance since October 2018 in Korea. However, the results of the NHS are not reported due to the absence of a follow-up tracking system. This study analyzed the status and the predicted referral rates of NHS after the Korean national health insurance coverage by analyzing the National Health Insurance Service database in 2019 and 2020. The NHS coverage was 91.7% of total birth in 2019 and 92.1% in 2020. The predicted referral rate of NHS calculated by the duplicated NHS cases was 1.05% in 2019 and 0.99% in 2020. However, another predicted referral rate calculated by the number of diagnostic auditory brainstem responses (ABRs) performed was 1.44% in 2019 and 1.43% in 2020. The first NHS was performed within one day of birth for 96.5% of the babies and within three days of birth for 97%. However, diagnostic ABR was adequately performed within three months of birth for only 4.3%, while 82.3% performed the test after six months which delays appropriate intervention for hearing loss. National support such as national coordinators, follow-up tracking, and data management systems are needed for early hearing detection and intervention of newborns and infants in Korea. Full article
(This article belongs to the Special Issue 3rd Edition of Big Data, Decision Models, and Public Health)
13 pages, 1288 KiB  
Article
Sleep Quality among the Elderly in 21st Century Shandong Province, China: A Ten-Year Comparative Study
by Zenghe Yue, Yi Zhang, Xiaojing Cheng and Jingxuan Zhang
Int. J. Environ. Res. Public Health 2022, 19(21), 14296; https://doi.org/10.3390/ijerph192114296 - 01 Nov 2022
Cited by 3 | Viewed by 1414
Abstract
Background: Despite the enormous changes observed in China since entering the 21st century, little is known about changes in sleep quality among older adults. Aims: The purpose of this study is to explore the changes, features, and influence factors of sleep quality among [...] Read more.
Background: Despite the enormous changes observed in China since entering the 21st century, little is known about changes in sleep quality among older adults. Aims: The purpose of this study is to explore the changes, features, and influence factors of sleep quality among the elderly in a ten-year period, providing evidence for sleep-quality enhancement. Methods: The data were obtained from the data of epidemiological sampling surveys on mental disorders in Shandong province in 2004 and 2015. A total of 4451 subjects (aged ≥ 60 years) in 2004 and 10,894 subjects (aged ≥ 60 years) in 2015 were selected by the multistage stratified sampling method. The demographic information and Pittsburgh Sleep Quality Index (PSQI) were collected. Results: The adjusted 1-month prevalence of poor sleep in 2015 was 22.5% (95% CI:21.7–23.3), which is lower than that in 2004 (24.8%) (95% CI:23.5–26.0, p = 0.002). The total score of the PSQI in 2015 (4.74 ± 3.96) was lower than that in 2004 (4.97 ± 4.18, p = 0.002). In 2015, a binary multi-factor logistic and stepwise regression analysis showed that being female, living in a rural area, living alone, being older, spending less years in school, and being jobless/unemployed made the participants more likely to develop poor sleep (p < 0.05, p < 0.01). Conclusions: In 2015, the overall sleep quality of the elderly (aged ≥ 60) in Shandong province was significantly improved compared to 2004. After more than 10 years, the characteristics of the elderly with sleep disturbances in Shandong province has changed. Therefore, more attention should be paid to gender, location of residence (rural or urban), living arrangement, age, education, occupation, and other factors to improve the sleep quality of the elderly. Full article
(This article belongs to the Special Issue 3rd Edition of Big Data, Decision Models, and Public Health)
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17 pages, 677 KiB  
Article
Deep Mobile Linguistic Therapy for Patients with ASD
by Ari Ernesto Ortiz Castellanos, Chuan-Ming Liu and Chongyang Shi
Int. J. Environ. Res. Public Health 2022, 19(19), 12857; https://doi.org/10.3390/ijerph191912857 - 07 Oct 2022
Cited by 1 | Viewed by 1571
Abstract
Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able [...] Read more.
Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able to communicate using language or speech. Many experts propose that continued therapy in the form of software training in this area might help to bring improvement. In this work, we propose a design of software speech therapy system for ASD. We combined different devices, technologies, and features with techniques of home rehabilitation. We used TensorFlow for Image Classification, ArKit for Text-to-Speech, Cloud Database, Binary Search, Natural Language Processing, Dataset of Sentences, and Dataset of Images with two different Operating Systems designed for Smart Mobile devices in daily life. This software is a combination of different Deep Learning Technologies and makes Human–Computer Interaction Therapy very easy to conduct. In addition, we explain the way these were connected and put to work together. Additionally, we explain in detail the architecture of software and how each component works together as an integrated Therapy System. Finally, it allows the patient with ASD to perform the therapy anytime and everywhere, as well as transmitting information to a medical specialist. Full article
(This article belongs to the Special Issue 3rd Edition of Big Data, Decision Models, and Public Health)
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9 pages, 315 KiB  
Article
Impact of LncRNA GAS5 Genetic Variants and the Epidermal Growth Factor Receptor Phenotypes on the Clinicopathological Characteristics of Lung Adenocarcinoma Patients
by Ming-Hong Hsieh, Yi-Liang Wu, Thomas Chang-Yao Tsao, Yi-Wen Huang, Jian-Cheng Lin, Chia-Yi Lee, Ming-Ju Hsieh and Shun-Fa Yang
Int. J. Environ. Res. Public Health 2022, 19(16), 9971; https://doi.org/10.3390/ijerph19169971 - 12 Aug 2022
Cited by 5 | Viewed by 1347
Abstract
The aim of the current study was to evaluate the combined effect of the single nucleotide polymorphism (SNP) in long non-coding RNA growth arrest-specific 5 (GAS5) and the phenotypes of epidermal growth factor receptor (EGFR) on the clinicopathological characteristics of lung adenocarcinoma. The [...] Read more.
The aim of the current study was to evaluate the combined effect of the single nucleotide polymorphism (SNP) in long non-coding RNA growth arrest-specific 5 (GAS5) and the phenotypes of epidermal growth factor receptor (EGFR) on the clinicopathological characteristics of lung adenocarcinoma. The present study examined the relationship between the GAS5 single-nucleotide polymorphisms (SNPs; rs145204276 Ins/Del, rs55829688 T/C) and the clinicopathological factors in 539 lung adenocarcinoma patients with or without EGFR mutations. We found that the genotype distributions of the two GAS5 SNPs between different EGFR genotypes were similar after adjusting for age, gender and smoking history. The GAS5 SNP rs145204276 Ins/Del + Del/Del illustrated a higher distribution with an advanced tumor stage (p = 0.030), larger tumor T status (p = 0.019), positive lymph node status (p = 0.014) and distal metastases (p = 0.011) in the EGFR wild type group. In the subgroup analysis of the EGFR wild type population, the presence of GAS5 SNP rs145204276 Ins/Del + Del/Del was correlated to an advanced tumor stage (p = 0.014) and distal metastases (p = 0.020) in non-smokers. In conclusion, these data indicate that the GAS5 SNP rs145204276 variant may help predict tumor stage, lymph node metastasis and distal metastases in patients with EGFR wild type lung adenocarcinoma. Full article
(This article belongs to the Special Issue 3rd Edition of Big Data, Decision Models, and Public Health)
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