Computer-assisted patient identification tool in inborn errors of metabolism - potential for rare disease patient registry and big data analysis.

BACKGROUND: Patient registries are crucial for rare disease management. However, manual registry construction is labor-intensive and often not user-friendly. Our goal is to establish Hong Kong's first computer-assisted patient identification tool for rare diseases, starting with inborn errors of metabolism (IEM). METHODS: Patient data from 2010 to 2019 was retrieved from electronic databases. Through big data analytics, patient data were filtered based on specific IEM-related biochemical and genetic tests. Clinical notes were analyzed using a rule-based natural language processing technique called regular expression. The algorithm classified each extracted paragraph as "IEM-related" or "not IEM-related." Pathologists reviewed the paragraphs for curation, and the algorithm's performance was evaluated. RESULTS: Out of 46,419 patients with IEM-related tests, the algorithm identified 100 as "IEM-related." After pathologists' validation, 96 cases were confirmed as true IEM, with 1 uncertain case and 3 false positives. A secondary ascertainment yielded a sensitivity of 92.3% compared to our previously published IEM cohort. CONCLUSIONS: Our artificial intelligence approach provides a novel method to identify IEM patients, facilitating the creation of a centralized, computer-assisted rare disease patient registry at the local and national levels. This data can potentially be accessed by multiple stakeholders for collaborative research and to enhance healthcare management for rare diseases.

The Use of Big Data Analysis in Digital Empowerment of Sustainable Technological Innovation Management of Ecological Enterprises.

With the social economy's rapid progress and the popularization of environmental awareness, ecological enterprises have gradually become a crucial trend in the development of modern enterprises. This work intends to promote the development of ecological enterprises to a higher level. This work first analyzes the management mode of ecological enterprises in the context of big data in China. Then, it establishes various indicators to analyze the role of sustainable technological innovation in enterprise development and the impact of digital empowerment on enterprise development. Finally, this work takes China's manufacturing industry and ecological enterprises in Hubei Province as examples to summarize the digital empowerment of sustainable technological innovation management of ecological enterprises under the background of big data. The final result indicates that sustainable technological innovation significantly reduces ecological enterprises' resource consumption and waste emissions. Additionally, it has a significant positive effect on improving enterprise output value and economic benefits. The digital empowerment of enterprises has a significant driving effect on sustainable technological innovation, with a digital driving coefficient of 26. This work provides a feasible scheme for the specific application of big data analysis in the technology innovation management of ecological enterprises, including market demand analysis, environmental monitoring and governance, technology assessment and risk management. This work expounds the role of big data analysis technology in improving decision-making efficiency, optimizing resource allocation and enhancing the competitiveness of enterprises in the digital empowerment of ecological enterprises.

Visualization and Improvement of the Quality, Efficiency, and Equity of the Healthcare System - Secondary Publication.

In a depopulating society, it is difficult to ensure sufficient resources and finances for health and health care. Thus, effective management of the reform of the healthcare system by visualizing the quality, efficiency, and equity of health care is imperative. This article presents an overview of the studies conducted by my team in this area over the past 35 years, covering the following four sections: (1) visualization of healthcare system using individual-level data, (2) healthcare system at the organizational level, (3) healthcare system at the national and regional levels, and (4) creation of a social system for health. To improve the quality, efficiency, and equity of the healthcare system as well as the social system for people's health, it is necessary to visualize the actual situation and share this information with all stakeholders to contribute to the joint management of healthcare system. On this basis, from the perspectives of each region and the nation, it is important to visualize and grasp various wider determinants of people's health and healthcare performance and to improve health care and social systems.

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST.

BACKGROUND: The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored. OBJECTIVE: This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames. METHODS: We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality. RESULTS: Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports. CONCLUSIONS: Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.

Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review.

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

From sky to road: Incorporating the satellite imagery into analysis of freight truck-related crash factors.

Freight truck-related crashes in urban contexts have caused significant economic losses and casualties, making it increasingly essential to understand the spatial patterns of such crashes. Limitations regarding data availability have greatly undermined the generalizability and applicability of certain prior research findings. This study explores the potential of emerging geospatial data to delve deeply into the determinants of these incidents with a more generalizable research design. By synergizing high-resolution satellite imagery with refined GIS map data and geospatial tabular data, a rich tapestry of the road environment and freight truck operations emerges. To navigate the challenges of zero-inflated issues of the crash datasets, the Tweedie Gradient Boosting model is adopted. Results reveal a pronounced spatial heterogeneity between highway and urban non-highway road networks in crash determinants. Factors such as freight truck activity, intricate road network patterns, and vehicular densities rise to prominence, albeit with varying degrees of influence across highways and urban non-highway terrains. Results emphasize the need for context-specific interventions for policymakers, encompassing optimized urban planning, infrastructural overhauls, and refined traffic management protocols. This endeavor may not only elevate the academic discourse around freight truck-related crashes but also champion a data-driven approach towards safer road ecosystems for all.

Preparation and Challenges in Developing a Big Data Analysis Framework in Occupational Medicine in Indonesia.

This mini review explores the transformative potential of big data analysis and artificial intelligence (AI) in reforming occupational medicine in Indonesia. Emphasizing the preconditions, case studies, and benefits, it underscores the role of big data in enhancing worker well-being. The review highlights the importance of informative health big data, especially in high-risk industries, with examples of case studies of AI implementation in occupational medicine during the COVID-19 pandemic and other relevant scenarios. While acknowledging the challenges of AI implementation, the essay identifies the role of academic and professional organizations as pioneers in big data utilization. Six potential benefits that are identified, including improved patient care and efficient resource allocation, demonstrate the transformative impact of big data analysis. The proposed pathway of preparation underscores the need for awareness, skill enhancement, and collaboration, addressing challenges in data management and stakeholder engagement. The conclusion emphasizes continuous assessment, feasibility studies, and commitment as essential steps in advancing occupational medicine through big data analysis.

Obstructive Sleep Apnea: A Contributing Factor in Gout.

INTRODUCTION: Obstructive sleep apnea (OSA) is a comorbidity, which has shared risk factors with gout as well as causes pathophysiological mechanisms causing hyperuricemia. The relationship remains contentious. METHODS: TrinetX, a global federated research network that provides a dataset of electronic medical records from different healthcare organizations (HCOs). We utilized this network to query patients who had a BMI greater than 30 and then two subgroups were made based on the presence or absence of OSA. Furthermore, propensity score matching (PSM) was carried out to match age, sex, race, chronic kidney disease (CKD), heart failure, and the use of diuretics. Compare outcome analytic function was utilized to map the co-relation with Gout. RESULTS: A total of 3541566 patients who had a BMI >30 were identified, out of which 817638 (23.09%) patients had OSA. 7.19% of patients with OSA had gout while 2.84% without OSA had gout (p<0.0001). The odds of having gout are 2.65 times higher in patients with OSA than patients without OSA (hazard ratio is 2.393, 95% confidence interval (CI) 2.367-2.419, p<0.0001). After PSM, both the groups of obese patients with and without International Classification of Diseases, 10th Revision (ICD-10) diagnosis of OSA included 801526 patients, within which 6.93% of patients with OSA had gout while 4.63% of patients without OSA had gout (p<0.0001). The odds ratio was 1.533 (95% CI 1.512-1.554, p<0.0001) and the hazard ratio was 1.404 (95% CI 1.386-1.423). CONCLUSION: Our study demonstrated that there is a strong correlation between gout and OSA. Chronic hypoxia-induced hyperuricemia is the most widespread explanation. OSA is a treatable condition with timely diagnosis and proper treatment. Prospective cohort studies are required to further test the strength of the relationship between OSA and gout.

Statistical Methods and Machine Learning Algorithms for Investigating Metabolic Syndrome in Temporomandibular Disorders: A Nationwide Study.

The objective of this study was to analyze the associations between temporomandibular disorders (TMDs) and metabolic syndrome (MetS) components, consequences, and related conditions. This research analyzed data from the Dental, Oral, Medical Epidemiological (DOME) records-based study which integrated comprehensive socio-demographic, medical, and dental databases from a nationwide sample of dental attendees aged 18-50 years at military dental clinics for 1 year. Statistical and machine learning models were performed with TMDs as the dependent variable. The independent variables included age, sex, smoking, each of the MetS components, and consequences and related conditions, including hypertension, hyperlipidemia, diabetes, impaired glucose tolerance (IGT), obesity, cardiac disease, obstructive sleep apnea (OSA), nonalcoholic fatty liver disease (NAFLD), transient ischemic attack (TIA), stroke, deep venous thrombosis (DVT), and anemia. The study included 132,529 subjects, of which 1899 (1.43%) had been diagnosed with TMDs. The following parameters retained a statistically significant positive association with TMDs in the multivariable binary logistic regression analysis: female sex [OR = 2.65 (2.41-2.93)], anemia [OR = 1.69 (1.48-1.93)], and age [OR = 1.07 (1.06-1.08)]. Features importance generated by the XGBoost machine learning algorithm ranked the significance of the features with TMDs (the target variable) as follows: sex was ranked first followed by age (second), anemia (third), hypertension (fourth), and smoking (fifth). Metabolic morbidity and anemia should be included in the systemic evaluation of TMD patients.

Prediction of the severity of exceeding design traffic loads on highway bridges.

Being a driver of failure consequences, forecasting the severity of events where design traffic load limits on bridges have been exceeded (DLEEs) is fundamental for road safety. Previous research has focused on estimating failure consequences by direct and indirect cost metrics. Only recently has research assessed severity unconventionally, in which the type of DLEEs was predicted by applying econometric models through Binomial Logistic Regression (BLR). Since machine learning models using Artificial Neural Networks (ANN) have not yet been explored, this study will enhance the literature as follows. First, two different 'severity' models were set up as a function of bridge-side, temporal-context, and traffic load hazard variables. Whilst the former relied on a BLR, the latter used an ANN. Second, the performance of these models was assessed using confusion matrixes, some performance indicators, and a cross-entropy parameter. Raw Weigh-In-Motion data on 7.4 M+ individual vehicle transits on a bridge along a primary roadway in Brescia (Italy) were processed. Although a similarly strong performance was achieved for BLR and ANN, the results indicated that ANN was able to predict severity records with a higher level of confidence than BLR on the case study dataset, with the cross-entropy of the ANN less than one third of that of the BLR. These analyses can support road authority traffic management to safeguard bridges from traffic load hazards. Finally, this study recommends future developments, such as considering the structural effects of traffic loads in the modelling, prioritizing traffic management actions among bridges at network level, and exploring the impact of ANN models in risk assessment.

Determining Optimal Cut-Off Value of Pancreatic-Cancer-Induced Total Cholesterol and Obesity-Related Factors for Developing Exercise Intervention: Big Data Analysis of National Health Insurance Sharing Service Data.

This study aimed to examine the effects of multiple parameters on the incidence of pancreatic cancer. We analyzed data from 1,108,369 individuals in the National Health Insurance Sharing Service Database (NHISS DB; birth to death; 2002 to 2015) and identified 2912 patients with pancreatic cancer. Body mass index, systolic/diastolic blood pressure, and fasting blood glucose and total cholesterol concentrations were lower in women with than without pancreatic cancer (p < 0.01). Fasting blood glucose and total cholesterol concentrations were significantly different between men with and without pancreatic cancer (p < 0.05). In the logistic regression analysis, the total cholesterol concentration (odds ratio (OR), 1.007; 95% confidence interval (CI), 1.005-1.010) was significantly higher in men than women with pancreatic cancer (p < 0.05). Pancreatic cancer rates were highest in men who smoked for 5-9 years or more (OR, 5.332) and in women who smoked for 10-19 years (OR, 18.330). Daily intensive exercise reduced the risk of pancreatic cancer by 56% in men (95% CI, 0.230-0.896). Receiver operating characteristic curve analysis revealed a total cholesterol concentration cut-off point of 188.50 mg/dL (p < 0.05) in men with pancreatic cancer, with a sensitivity and specificity of 53.5% and 54.6%, respectively. For women, the cut-off values for weight and gamma glutamyl transpeptidase concentration were 58.5 kg and 20.50 U/L, respectively. The sex-specific differences in patients with pancreatic cancer identified herein will aid in the development of individualized evidence-based prognostic and preventive programs for the treatment of pancreatic cancer.

[Application and Progress of Intelligent Assistant Diagnosis in Mental Health Assessment System].

The establishment of mental health assessment system provides a new way for the early diagnosis of mental health problems, in view of the growing population of mental diseases and problems and the uneven distribution of mental health resources. In the mental health assessment system, intelligent assistant diagnosis can assist or help psychiatrists improve their work efficiency. Intelligent assistant diagnosis provides technical support for predictive screening and auxiliary diagnosis of mental health problems. It is an intelligent diagnosis research based on big data analysis and machine learning in mental health assessment system. This article mainly reviews the application methods, the application progress in the field of mental health, as well as related technical issues and safety issues, and prospects the future research development.

Evaluation of a data-driven intelligent waste classification system for scientific management of garbage recycling in a Chinese community.

Waste classification management is effective in addressing the increasing waste output and continuous deterioration of environmental conditions. The waste classification behaviour of resident is an important basis for managers to collect and allocate resources. Traditional analysis methods, such as questionnaire, have limitations considering the complexity of individual behaviour. An intelligent waste classification system (IWCS) was applied and studied in a community for 1 year. Time-based data analysis framework was constructed to describe the residents' waste sorting behaviour and evaluate the IWCS. The results showed that residents preferred to use face recognition than other modes of identification. The ratio of waste delivery frequency was 18.34% in the morning and 81.66% in the evening, respectively. The optimal time windows of disposing wastes were from 6:55 to 9:05 in the morning and from 18:05 to 20:55 in the evening which can avoid crowding. The percentage of accuracy of waste disposal increased gradually in a year. The amount of waste disposal was largest on every Sunday. The average accuracy was more than 94% based on monthly data, but the number of participating residents decreased gradually. Therefore, the study demonstrates that IWCS is a potential platform for increasing the accuracy and efficiency of waste disposal and can promote regulations implementation.

Biochar application to reduce CO2 emissions from farmland and increase crop yields: a 22-year study based on big data analysis and ideal scenario modeling.

How to reduce the emission of greenhouse gas CO2 from farmland and to improve crop yield is one of the most concerned agricultural ecological environment issues for scientists at present. As an excellent soil conditioner, biochar has a very broad research value and application path in the field. Taking farmland in northern China as the research object, this paper studied the impact of biochar application on soil CO2 emission potential and crop yield in farmland in northern China through big data analysis and modeling methods. The results show that the ideal scenario for increasing crop production and reducing CO2 emissions should be that the raw materials for the preparation of biochar are wheat straw and rice straw; the preparation temperature is 400-500 â„ƒ, the C/N ratio of biochar is 80-90, the pH of biochar is 8-9, the soil texture is sandy soil or loam soil, soil bulk density is 1.2-1.4 g cm-3, the soil pH is less than 6, the soil organic matter content is 10-20 g kg-1, and the soil C/N is less than 10; the application amount of biochar is 20-40 t ha-1; and the use time of biochar is 1 year. In view of this, this study selected the data of microbial biomass (X1), soil respiration rate (X2), soil organic matter (X3), soil moisture content (X4), average soil temperature (X5), and CO2 emissions (Y) for correlation analysis and path analysis, and finally obtained the multiple stepwise regression equation between CO2 emissions and various impact factors as follows: Y = - 27.981 + 0.6249 X1 + 0.5143 X2 + 0.4257X3 + 0.3165X4 + 0.2014X5 (R2 = 0.867, P < 0.01, n = 137). Microbial biomass and soil respiration rate directly affect CO2 emissions, reaching a highly significant level (P < 0.01); the second is soil organic matter, soil moisture content, and average soil temperature. The indirect relationship between CO2 emissions and soil average temperature, microbial biomass, and soil respiration rate is the strongest, followed by soil organic matter and soil moisture content.

Data-Driven Analytics to Discover APRN's Impact on Nursing Home Hospitalization: Causal Discovery Analysis.

OBJECTIVES: Research shows advanced practice registered nurses (APRNs) embedded in nursing homes (NHs) reduce resident hospitalizations. However, the specific APRN activities that reduce hospitalizations have not been adequately investigated. This study aims to identify the causal links between APRN activities and NHs resident hospitalization. The study also examined relationships among other variables, including advanced directives, clinical diagnosis, and length of hospitalization. DESIGN: Secondary data analysis. SETTING AND PARTICIPANTS: Residents of NHs participating in the Missouri Quality Initiative for Nursing Homes, 2016-2019. METHODS: We performed a secondary analysis of data from the Missouri Quality Initiative for Nursing Homes Intervention using causal discovery analysis, a machine learning, data-driven technique to determine causal relationships across data. The resident roster and INTERACT resident hospitalization datasets were combined to create the final dataset. Variables in the analysis model were divided into before and after hospitalization. Expert consensus was used to validate and interpret the outcomes. RESULTS: The research team analyzed 1161 hospitalization events and their associated NH activities. APRNs evaluated NH residents before a transfer, expedited follow-up nursing assessments, and authorized hospitalization when necessary. No significant causal relationships were found between APRN activities and the clinical diagnosis of a resident. The analysis also showed multifaceted relationships related to having advanced directives and duration of hospitalization. CONCLUSIONS AND IMPLICATIONS: This study demonstrated the importance of APRNs embedded in NHs to improve resident outcomes. APRNs in NHs can facilitate communication and collaboration among the nursing team, leading to early identification and treatment for resident status changes. APRNs can also initiate more timely transfers by reducing the need for physician authorization. These findings emphasize the crucial role of APRNs in NHs and suggest that budgeting for APRN services may be an effective strategy to reduce hospitalizations. Additional findings regarding advance directives are discussed.

Design and analysis of management platform based on financial big data.

Traditional financial accounting will become limited by new technologies which are unable to meet the market development. In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. In addition, a financial data management platform based on distributed Hadoop architecture is designed, which combines MapReduce framework with the fuzzy clustering algorithm and the local outlier factor (LOF) algorithm, and uses MapReduce to operate in parallel with the two algorithms, thus improving the performance of the algorithm and the accuracy of the algorithm, and helping to improve the operational efficiency of enterprise financial data processing. The comparative experimental results show that the proposed platform can achieve the best the running efficiency and the accuracy of financial data classification compared with other methods, which illustrate the effectiveness and superiority of the proposed platform.

An indoor location-based hospital Porter management system and trace analysis.

Porters play an important role in supporting hospital operations. Their responsibilities include transporting patients and medical equipment between wards and departments. They also need to deliver specimens, drugs, and patients' notes to the correct place at the right time. Therefore, maintaining a trustworthy and reliable porter team is crucial for hospitals to ensure the quality of patient care and smooth the flow of daily operations. However, most existing porter systems lack detailed information about the porter movement process. For example, the location of porters is not transparent to the dispatch center. Thus, the dispatcher does not know if porters are spending all their time providing services. The invisibility makes it difficult for hospitals to assess and improve the efficiency of porter operations. In this work, we first developed an indoor location-based porter management system (LOPS) on top of the infrastructure of indoor positioning services in the hospital National Taiwan University Hospital YunLin Branch. The LOPS provides real-time location information of porters for the dispatcher to prioritize tasks and manage assignments. We then conducted a 5-month field trial to collect porters' traces. Finally, a series of quantitative analyses were performed to assess the efficiency of porter operations, such as the movement distribution of porters in different time periods and areas, workload distribution among porters, and possible bottlenecks of delivering services. Based on the analysis results, recommendations were given to improve the efficiency of the porter team.

Big data analysis of water quality monitoring results from the Xiang River and an impact analysis of pollution management policies.

Water pollution prevention and control of the Xiang River has become an issue of great concern to China's central and local governments. To further analyze the effects of central and local governmental policies on water pollution prevention and control for the Xiang River, this study performs a big data analysis of 16 water quality parameters from 42 sections of the mainstream and major tributaries of the Xiang River, Hunan Province, China from 2005 to 2016. This study uses an evidential reasoning-based integrated assessment of water quality and principal component analysis, identifying the spatiotemporal changes in the primary pollutants of the Xiang River and exploring the correlations between potentially relevant factors. The analysis showed that a series of environmental protection policies implemented by Hunan Province since 2008 have had a significant and targeted impact on annual water quality pollutants in the mainstream and tributaries. In addition, regional industrial structures and management policies also have had a significant impact on regional water quality. The results showed that, when examining the changes in water quality and the effects of pollution control policies, a big data analysis of water quality monitoring results can accurately reveal the detailed relationships between management policies and water quality changes in the Xiang River. Compared with policy impact evaluation methods primarily based on econometric models, such a big data analysis has its own advantages and disadvantages, effectively complementing the traditional methods of policy impact evaluations. Policy impact evaluations based on big data analysis can further improve the level of refined management by governments and provide a more specific and targeted reference for improving water pollution management policies for the Xiang River.

Big data analytics and smart cities: applications, challenges, and opportunities.

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis-Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study.

This study aimed to analyze the associations of obstructive sleep apnea (OSA) with dental parameters while controlling for socio-demographics, health-related habits, and each of the diseases comprising metabolic syndrome (MetS), its consequences, and related conditions. We analyzed data from the dental, oral, and medical epidemiological (DOME) cross-sectional records-based study that combines comprehensive socio-demographic, medical, and dental databases of a nationally representative sample of military personnel for one year. Analysis included statistical and machine learning models. The study included 132,529 subjects; of these, 318 (0.2%) were diagnosed with OSA. The following parameters maintained a statistically significant positive association with OSA in the multivariate binary logistic regression analysis (descending order from highest to lowest OR): obesity (OR = 3.104 (2.178-4.422)), male sex (OR = 2.41 (1.25-4.63)), periodontal disease (OR = 2.01 (1.38-2.91)), smoking (OR = 1.45 (1.05-1.99)), and age (OR = 1.143 (1.119-1.168)). Features importance generated by the XGBoost machine learning algorithm were age, obesity, and male sex (located on places 1-3), which are well-known risk factors of OSA, as well as periodontal disease (fourth place) and delivered dental fillings (fifth place). The Area Under Curve (AUC) of the model was 0.868 and the accuracy was 0.92. Altogether, the findings supported the main hypothesis of the study, which was that OSA is linked to dental morbidity, in particular to periodontitis. The findings highlight the need for dental evaluation as part of the workup of OSA patients and emphasizes the need for dental and general medical authorities to collaborate by exchanging knowledge about dental and systemic morbidities and their associations. The study also highlights the necessity for a comprehensive holistic risk management strategy that takes systemic and dental diseases into account.