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
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 and Collections 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 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 (8 papers)

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Research

Article
Absence of Association between Previous Mycoplasma pneumoniae Infection and Subsequent Myasthenia Gravis: A Nationwide Population-Based Matched Cohort Study
Int. J. Environ. Res. Public Health 2021, 18(14), 7677; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147677 - 19 Jul 2021
Viewed by 337
Abstract
Mycoplasma pneumoniae (M. pneumoniae) is not only one of the most common pathogenic bacteria for respiratory infection but also a trigger for many autoimmune diseases. Its infection process shared many similarities with the pathogenesis of myasthenia gravis (MG) at cellular and [...] Read more.
Mycoplasma pneumoniae (M. pneumoniae) is not only one of the most common pathogenic bacteria for respiratory infection but also a trigger for many autoimmune diseases. Its infection process shared many similarities with the pathogenesis of myasthenia gravis (MG) at cellular and cytokine levels. Recent case reports demonstrated patients present with MG after M. pneumoniae infection. However, no epidemiological studies ever looked into the association between the two. Our study aimed to investigate the relationship between M. pneumoniae infection and subsequent development of MG. In this population-based retrospective cohort study, the risk of MG was analyzed in patients who were newly diagnosed with M. pneumoniae infection between 2000 and 2013. A total of 2428 M. pneumoniae patients were included and matched with the non-M. pneumoniae control cohort at a 1:4 ratio by age, sex, and index date. Cox proportional hazards regression analysis was applied to analyze the risk of MG development after adjusting for sex, age, and comorbidities, with hazard ratios and 95% confidence intervals. The incidence rates of MG in the non-M. pneumoniae and M. pneumoniae cohorts were 0.96 and 1.97 per 10,000 person-years, respectively. Another case–control study of patients with MG (n = 515) was conducted to analyze the impact of M. pneumoniae on MG occurrence as a sensitivity analysis. The analysis yielded consistent absence of a link between M. pneumoniae and MG. Although previous studies have reported that M. pneumoniae infection and MG may share associated immunologic pathways, we found no statistical significance between M. pneumoniae infection and subsequent development of MG in this study. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
Exploring the Effect of Social Support and Empathy on User Engagement in Online Mental Health Communities
Int. J. Environ. Res. Public Health 2021, 18(13), 6855; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18136855 - 26 Jun 2021
Viewed by 423
Abstract
It is known that social support and empathy are beneficial for mental health. As a result of the widespread development of social media, online social support and empathy could also influence user behaviors during the development of online communities. However, few studies have [...] Read more.
It is known that social support and empathy are beneficial for mental health. As a result of the widespread development of social media, online social support and empathy could also influence user behaviors during the development of online communities. However, few studies have examined these effects from the perspective of online mental health communities. These communities appear to be a crucial source for mental health related support, but the spread of online empathy in these communities is not well-understood. This study focused on 22 mental health related subreddits, and matched and compared users (1) who received social support with those who did not receive social support, and users (2) who received more empathic social support with those who received less empathic social support. The results showed that social support and empathy are “contagious”. That is, users who received social support at their first post would be more likely to post again and provide support for others; in addition, users who received more empathic support would subsequently express a higher level of empathy to others in the future. Our findings indicate the potential chain reaction of social support and empathy in online mental health communities. Our study also provides insights into how online mental health communities might better assist people to deliver social support that can help others to deal with mental problems. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
A Nationwide Population-Based Study on the Association between Land Transport Accident and Peripheral Vestibular Disorders
Int. J. Environ. Res. Public Health 2021, 18(12), 6570; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18126570 - 18 Jun 2021
Viewed by 380
Abstract
This case–control study aimed to investigate the association of peripheral vestibular disorders (PVD) with subsequent land transport accidents. Data for this study were obtained from Taiwan’s National Health Insurance (NHI) dataset. We retrieved 8704 subjects who were newly found to have land transport [...] Read more.
This case–control study aimed to investigate the association of peripheral vestibular disorders (PVD) with subsequent land transport accidents. Data for this study were obtained from Taiwan’s National Health Insurance (NHI) dataset. We retrieved 8704 subjects who were newly found to have land transport accidents as cases. Their diagnosis date was used as their index date. Controls were identified by propensity score matching (one per case, n = 8704 controls) from the NHI dataset with their index date being the date of their first health service claim in 2017. Multiple logistic regressions were performed to calculate the prior PVD odds ratio of cases vs. controls. We found that 2.36% of the sampled patients had been diagnosed with PVD before the index date, 3.37% among cases and 1.36% among controls. Chi-square test revealed that there was a significant association between land transport accident and PVD (p < 0.001). Furthermore, multiple logistic regression analysis suggested that cases were more likely to have had a prior PVD diagnosis when compared to controls (OR = 2.533; 95% CI = 2.041–3.143; p < 0.001). After adjusting for age, gender, hypertension, diabetes, coronary heart disease, and hyperlipidemia, cases had a greater tendency to have a prior diagnosis of PVD than controls (OR = 3.001, 95% CI = 2.410–3.741, p < 0.001). We conclude that patients with PVD are at twofold higher odds for land transport accidents. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
Article
Long-Term Medical Resource Consumption between Surgical Clipping and Endovascular Coiling for Aneurysmal Subarachnoid Hemorrhage: A Propensity Score–Matched, Nationwide, Population-Based Cohort Study
Int. J. Environ. Res. Public Health 2021, 18(11), 5989; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18115989 - 02 Jun 2021
Viewed by 727
Abstract
Purpose: To estimate long-term medical resource consumption in patients with subarachnoid aneurysmal hemorrhage (SAH) receiving surgical clipping or endovascular coiling. Patients and methods: From Taiwan’s National Health Insurance Research Database, we enrolled patients with aneurysmal SAH who received clipping or coiling. After propensity [...] Read more.
Purpose: To estimate long-term medical resource consumption in patients with subarachnoid aneurysmal hemorrhage (SAH) receiving surgical clipping or endovascular coiling. Patients and methods: From Taiwan’s National Health Insurance Research Database, we enrolled patients with aneurysmal SAH who received clipping or coiling. After propensity score matching and adjustment for confounders, a generalized linear mixed model was used to determine significant differences in the accumulative hospital stay (days), intensive care unit (ICU) stay, and total medical cost for aneurysmal SAH, as well as possible subsequent surgical complications and recurrence. Results: The matching process yielded a final cohort of 8102 patients (4051 and 4051 in endovascular coil embolization and surgical clipping, respectively) who were eligible for further analysis. The mean accumulative hospital stay significantly differed between coiling (31.2 days) and clipping (46.8 days; p < 0.0001). After the generalized linear model adjustment of gamma distribution with a log link, compared with the surgical clipping procedure, the adjusted odds ratios (aOR; 95% confidence interval [CI]) of the medical cost of accumulative hospital stay for the endovascular coil embolization procedure was 0.63 (0.60, 0.66; p < 0·0001). The mean accumulative ICU stay significantly differed between the coiling and clipping groups (9.4 vs. 14.9 days; p < 0.0001). The aORs (95% CI) of the medical cost of accumulative ICU stay in the endovascular coil embolization group was 0.61 (0.58, 0.64; p < 0.0001). The aOR (95% CI) of the total medical cost of index hospitalization in the endovascular coil embolization group was 0·85 (0.82, 0.87; p < 0.0001). Conclusions: Medical resource consumption in the coiling group was lower than that in the clipping group. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
Article
A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery
Int. J. Environ. Res. Public Health 2021, 18(5), 2713; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18052713 - 08 Mar 2021
Cited by 1 | Viewed by 614
Abstract
Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. [...] Read more.
Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients’ previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician–patient communication to achieve better patient comprehension. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
Clinical Knowledge Supported Acute Kidney Injury (AKI) Risk Assessment Model for Elderly Patients
Int. J. Environ. Res. Public Health 2021, 18(4), 1607; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18041607 - 08 Feb 2021
Viewed by 731
Abstract
From the clinical viewpoint, the statistical approach is still the cornerstone for exploring many diseases. This study was conducted to explore the risk factors related to acute kidney injury (AKI) for elderly patients using the multiple criteria decision-making (MCDM) approach. Ten nephrologists from [...] Read more.
From the clinical viewpoint, the statistical approach is still the cornerstone for exploring many diseases. This study was conducted to explore the risk factors related to acute kidney injury (AKI) for elderly patients using the multiple criteria decision-making (MCDM) approach. Ten nephrologists from a teaching hospital in Taipei took part in forming the AKI risk assessment model. The key findings are: (1) Comorbidity and Laboratory Values would influence Comprehensive Geriatric Assessment; (2) Frailty is the highest influential AKI risk factor for elderly patients; and (3) Elderly patients could enhance their daily activities and nutrition to improve frailty and lower AKI risk. Furthermore, we illustrate how to apply MCDM methods to retrieve clinical experience from seasoned doctors, which may serve as a knowledge-based system to support clinical prognoses. In conclusion, this study has shed light on integrating multiple research approaches to assist medical decision-making in clinical practice. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
A Nationwide Population-Based Study on the Incidence of Parapharyngeal and Retropharyngeal Abscess—A 10-Year Study
Int. J. Environ. Res. Public Health 2021, 18(3), 1049; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18031049 - 25 Jan 2021
Viewed by 811
Abstract
This study aimed to investigate the annual incidence of parapharyngeal and retropharyngeal abscess (PRPA) based on 10-year population-based data. Patients with PRPA were identified from the Taiwan Health Insurance Research Database, a database of all medical claims of a randomly selected, population-representative sample [...] Read more.
This study aimed to investigate the annual incidence of parapharyngeal and retropharyngeal abscess (PRPA) based on 10-year population-based data. Patients with PRPA were identified from the Taiwan Health Insurance Research Database, a database of all medical claims of a randomly selected, population-representative sample of over two million enrollees of the National Health Insurance system that covers over 99% of Taiwan’s citizens. During 2007–2016, 5779 patients received a diagnosis of PRPA. We calculated the population-wide incidence rates of PRPA by sex and age group (20–44, 45–64, and >64) as well as in-hospital mortality. The annual incidence rate of PRPA was 2.64 per 100,000 people. The gender-specific incidence rates per 100,000 people were 3.34 for males and 1.94 for females with a male:female gender ratio of 1.72. A slight increase in incidence rates among both genders over the study period was noted. Age-specific rates were lowest in the 20–44 age group with a mean annual incidence of 2.00 per 100,000 people, and the highest rates were noted in the age groups of 45–64 and >64 years with mean annual incidences of 3.21 and 3.20, respectively. We found that PRPA is common in Taiwan, males and older individuals are more susceptible to it, and incidence has increased in recent years. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
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://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228603 - 19 Nov 2020
Viewed by 654
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)
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