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: 28 February 2022.

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 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 (15 papers)

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Research

Article
Use of Antibiotic Treatment in Pregnancy and the Risk of Several Neonatal Outcomes: A Population-Based Study
Int. J. Environ. Res. Public Health 2021, 18(23), 12621; https://doi.org/10.3390/ijerph182312621 - 30 Nov 2021
Viewed by 142
Abstract
Background: Limited evidence is available on the safety and efficacy of antimicrobials during pregnancy, with even less according to the trimester of their use. Objective: This study aimed to evaluate the association between exposure to antibiotics therapy (AT) during pregnancy and short-term neonatal [...] Read more.
Background: Limited evidence is available on the safety and efficacy of antimicrobials during pregnancy, with even less according to the trimester of their use. Objective: This study aimed to evaluate the association between exposure to antibiotics therapy (AT) during pregnancy and short-term neonatal outcomes. Methods: We considered 773,237 deliveries that occurred between 2007–2017 in the Lombardy region of Italy. We evaluated the risk of neonatal outcomes among infants that were born to mothers who underwent AT during pregnancy. The odds ratios and the hazard ratios, with the 95% confidence intervals, were estimated respectively for early (first/second trimester) and late (third trimester) exposure. The propensity score was used to account for potential confounders. We also performed subgroup analysis for the class of AT. Results: We identified 132,024 and 76,921 singletons that were exposed to AT during early and late pregnancy, respectively. Infants born to mothers with early exposure had 17, 11, and 16% increased risk of preterm birth, low birth weight, and low Apgar score, respectively. Infants that were exposed in late pregnancy had 25, 11, and 13% increased risk of preterm birth, low birth weight, and low Apgar score, respectively. The results were consistent in the subgroup analysis. Conclusion: Our results suggested an increased risk of several neonatal outcomes in women exposed to ATs during pregnancy, albeit we were not able to assess to what extent the observed effects were due to the infection itself. To reduce the risk of neonatal outcomes, women that are prescribed AT during pregnancy should be closely monitored. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
Effects of Mobile Application Program (App)-Assisted Health Education on Preventive Behaviors and Cancer Literacy among Women with Cervical Intraepithelial Neoplasia
Int. J. Environ. Res. Public Health 2021, 18(21), 11603; https://doi.org/10.3390/ijerph182111603 - 04 Nov 2021
Viewed by 495
Abstract
Objective: This study aimed (1) to study the effects of health education on preventive behaviors and cancer literacy among women with cervical intraepithelial neoplasia (CIN); (2) to compare the effects of mobile application program (App)-assisted health education with traditional book-form health education. Participants: [...] Read more.
Objective: This study aimed (1) to study the effects of health education on preventive behaviors and cancer literacy among women with cervical intraepithelial neoplasia (CIN); (2) to compare the effects of mobile application program (App)-assisted health education with traditional book-form health education. Participants: A total of 132 women ages 20 to 69 years women. Methods: This prospective longitudinal study enrolled 132 CIN women who were evaluated three times. Propensity score matching was used by controlling subjects’ age strata, body mass index, education level, occupation, and type of surgery. Results: The influences of various educational tools were investigated. Four domains were assessed, including health behavior, attitude towards behavior change, self-efficacy of behavior, and cervical cancer (CCa) literacy. Significant improvements in behavior change and CCa literacy due to a health education program were observed (p ≤ 0.002). The App combined with a traditional booklet had the highest score for behavior change and was significantly greater than the booklet-only learning (p = 0.002). The App-assisted form, either App alone or combined with booklet, had a significantly better impact on health promotion when compared to the booklet alone (p = 0.045 and 0.005, respectively). App-only learning had the highest score of CCa literacy (p = 0.004). Conclusion: Health education interventions can have positive effects in terms of change of behavior and CCa literacy. App-assisted learning could be used as a supportive technology, and App learning alone or combined with a traditional booklet may be an innovative model of clinical health promotion for women with CIN. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
Incorporating Patient Preferences into a Decision-Making Model of Hand Trauma Reconstruction
Int. J. Environ. Res. Public Health 2021, 18(21), 11081; https://doi.org/10.3390/ijerph182111081 - 21 Oct 2021
Viewed by 460
Abstract
Background: Few studies have addressed patient preferences in emergent surgical decision making. Aim of the study: Analyzing patient preferences for hand trauma reconstruction to propose a decision-making model. Methods: A conjoint analysis survey was developed with Sawtooth Software. Three common flaps—i.e., a cross-finger [...] Read more.
Background: Few studies have addressed patient preferences in emergent surgical decision making. Aim of the study: Analyzing patient preferences for hand trauma reconstruction to propose a decision-making model. Methods: A conjoint analysis survey was developed with Sawtooth Software. Three common flaps—i.e., a cross-finger flap (CFF), a dorsal metacarpal artery perforator flap (DMAPF), and an arterialized venous flap (AVF)—were listed as treatment alternatives. Five attributes corresponding to these flaps were recovery time, total procedure, postoperative care methods, postoperative scar condition, and complication rate. Utility and importance scores were generated from the software, and preference characteristics were evaluated using cluster analysis. Results: The survey was completed by 197 participants with hand trauma. Complication risk received the highest importance score (42.87%), followed by scar condition (21.55%). Cluster analysis classified the participants as “conservative,” “practical,” and “dual-concern”. The dual-concern and conservative groups had more foreign laborers and highly educated participants, respectively, than the other groups. Most participants in the conservative and practical groups preferred DMAPF, whereas those in the dual-concern group favored CFF. Our proposed model consisted of shared decision-making and treatment recommendation pathways. Conclusion: Incorporating patient preferences into the decision-making model can strengthen patient-centered care. Further research on the applications of the proposed model is warranted. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets
Int. J. Environ. Res. Public Health 2021, 18(20), 10811; https://doi.org/10.3390/ijerph182010811 - 14 Oct 2021
Viewed by 672
Abstract
With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further [...] Read more.
With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further applications in data analysis and decision-making. Essentially, the data can be divided into three types, namely, statistical, image-based, and sequential data. Each type has a different method of retrieval, processing, and deployment. Additionally, the application of machine learning (ML) and deep learning (DL) in healthcare support systems is growing more rapidly than ever. Numerous high-performance architectures are proposed to optimize decision-making. As reliability and stability are the most important factors in the healthcare support system, enhancing the predicted performance and maintaining the stability of the model are always the top priority. The main idea of our study comes from ensemble techniques. Numerous studies and data science competitions show that by combining several weak models into one, ensemble models can attain outstanding performance and reliability. We propose three deep ensemble learning (DEL) approaches, each with stable and reliable performance, that are workable on the above-mentioned data types. These are deep-stacked generalization ensemble learning, gradient deep learning boosting, and deep aggregation learning. The experiment results show that our proposed approaches achieve more vigorous and reliable performance than traditional ML and DL techniques on statistical, image-based, and sequential benchmark datasets. In particular, on the Heart Disease UCI dataset, representing the statistical type, the gradient deep learning boosting approach dominates the others with accuracy, recall, F1-score, Matthews correlation coefficient, and area under the curve values of 0.87, 0.81, 0.83, 0.73, and 0.91, respectively. On the X-ray dataset, representing the image-based type, the deep aggregation learning approach shows the highest performance with values of 0.91, 0.97, 0.93, 0.80, and 0.94, respectively. On the Depresjon dataset, representing the sequence type, the deep-stacked generalization ensemble learning approach outperforms the others with values of 0.91, 0.84, 0.86, 0.8, and 0.94, respectively. Overall, we conclude that applying DL models using our proposed approaches is a promising method for the healthcare support system to enhance prediction and diagnosis performance. Furthermore, our study reveals that these approaches are flexible and easy to apply to achieve optimal performance. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
Application of Standardized Proportional Mortality Ratio to the Assessment of Health Risk in Relatively Healthy Populations: Using a Study of Cancer Risk in Telecommunication Workers with Excess Exposure to Acid Mists as an Example
Int. J. Environ. Res. Public Health 2021, 18(18), 9870; https://doi.org/10.3390/ijerph18189870 - 19 Sep 2021
Viewed by 502
Abstract
When a study population is relatively healthy, such as an occupational population, epidemiological studies are likely to underestimate risk. We used a case study on the cancer risk of workers with exposure to acid mists, a well-documented carcinogen, to demonstrate that using proportional [...] Read more.
When a study population is relatively healthy, such as an occupational population, epidemiological studies are likely to underestimate risk. We used a case study on the cancer risk of workers with exposure to acid mists, a well-documented carcinogen, to demonstrate that using proportional mortality ratios (PMRs) is more appropriate than mortality ratios in assessing risk in terms of mortality. The study included 10,229 employees of a telecommunication company who worked in buildings with battery rooms. In these buildings, the battery rooms had the highest levels of sulfuric acid in the air (geometric mean = 10.7 μg/m3). With the general population in Taiwan as a reference, a decreased standardized mortality ratio (0.42, p < 0.01) from all causes combined, between 1 January 1985 and 31 December 1996, was observed, indicating a healthy worker effect. When we reanalyzed the data using standardized PMR, elevated risks were observed for all cancers combined (1.46, p = 0.01) and cancers of the digestive organs and peritoneum (1.61, p = 0.02), especially stomach cancer (2.94, p = 0.01). The results showed that PMR can detect increases in mortality when a study population is generally healthier than the comparison population and call for further studies on the possible carcinogenic effects of low-level acid mist exposures on the stomach. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
Article
Exploration of Preventable Hospitalizations for Colorectal Cancer with the National Cancer Control Program in Taiwan
Int. J. Environ. Res. Public Health 2021, 18(17), 9327; https://doi.org/10.3390/ijerph18179327 - 03 Sep 2021
Viewed by 567
Abstract
Background: Causing more than 40,000 deaths each year, cancer is one of the leading causes of mortality and preventable hospitalizations (PH) in Taiwan. To reduce the incidence and severity of cancer, the National Cancer Control Program (NCCP) includes screening for various types of [...] Read more.
Background: Causing more than 40,000 deaths each year, cancer is one of the leading causes of mortality and preventable hospitalizations (PH) in Taiwan. To reduce the incidence and severity of cancer, the National Cancer Control Program (NCCP) includes screening for various types of cancer. A cohort study was conducted to explore the long-term trends in PH/person-years following NCCP intervention from 1997 to 2013. Methods: Trend analysis was carried out for long-term hospitalization. The Poisson regression model was used to compare PH/person-years before (1997–2004) and after intervention (2005–2013), and to explore the impact of policy intervention. Results: The policy response reduced 26% for the risk of hospitalization; in terms of comorbidity, each additional point increased the risk of hospitalization by 2.15 times. The risk of hospitalization doubled for each 10-year increase but was not statistically significant. Trend analysis validates changes in the number of hospitalizations/person-years in 2005. Conclusions: PH is adopted as an indicator for monitoring primary care quality, providing governments with a useful reference for which to gauge the adequacy, accessibility, and quality of health care. Differences in PH rates between rural and urban areas can also be used as a reference for achieving equitable distribution of medical resources. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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Article
Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals
Int. J. Environ. Res. Public Health 2021, 18(16), 8700; https://doi.org/10.3390/ijerph18168700 - 17 Aug 2021
Cited by 3 | Viewed by 706
Abstract
Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions—a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk patients to all [...] Read more.
Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions—a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk patients to all public hospitals for customized discharge procedures within 24 h of admission. We analyzed all public hospital admissions (N = 150,322) in a year. Among four models, the gradient boosting machine performed the best (AUC = 0.79) with a positive predictive value set at 70%. Interestingly, the cumulative length of stay (LOS) in the past 12 months was a stronger predictor than the number of previous admissions, as it is a better proxy for acute care utilization. Another important predictor was the “number of days from previous non-elective admission”, which is different from previous studies that included both elective and non-elective admissions. Of note, the model did not include LOS of the index admission—a key predictor in other models—since our predictive model identified frequent admitters for pre-discharge interventions during the index (current) admission. The scientific ingredients that built the model did not guarantee its successful implementation—an “art” that requires the alignment of processes, culture, human capital, and senior management sponsorship. Change management is paramount, otherwise data-driven health policies, no matter how well-intended, may not be accepted or implemented. Overall, our study demonstrated the viability of using artificial intelligence (AI) to build a near real-time nationwide prediction tool for individual-centric discharge, and the critical factors for successful implementation. Full article
(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)
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://doi.org/10.3390/ijerph18147677 - 19 Jul 2021
Viewed by 749
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://doi.org/10.3390/ijerph18136855 - 26 Jun 2021
Viewed by 775
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://doi.org/10.3390/ijerph18126570 - 18 Jun 2021
Viewed by 679
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://doi.org/10.3390/ijerph18115989 - 02 Jun 2021
Viewed by 1101
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://doi.org/10.3390/ijerph18052713 - 08 Mar 2021
Cited by 4 | Viewed by 896
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://doi.org/10.3390/ijerph18041607 - 08 Feb 2021
Viewed by 948
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://doi.org/10.3390/ijerph18031049 - 25 Jan 2021
Viewed by 1021
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://doi.org/10.3390/ijerph17228603 - 19 Nov 2020
Cited by 1 | Viewed by 761
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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