scholarly journals Real-Time Inferential Analytics Based on Online Databases of Trends: A Breakthrough within the Discipline of Digital Epidemiology in Dentistry and Dental Anatomy

2018 ◽  
Author(s):  
Ahmed Al-Imam ◽  
Usama Khalid ◽  
Shahad Al-Qaisi ◽  
Nawfal Al-Hadithi ◽  
Dawoude Kaouche
Keyword(s):  
Statistical Analysis ◽  
Statistical Inference ◽  
Real Time ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Google Trends ◽  
Cochrane Library ◽  
Dental Anatomy ◽  
Digital Epidemiology ◽  
The Cochrane Library

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analysis and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

scholarly journals Real-Time Inferential Analytics Based on Online Databases of Trends: A Breakthrough within the Discipline of Digital Epidemiology in Dentistry and Dental Anatomy

2018 ◽  
Author(s):  
Ahmed Al-Imam ◽  
Usama Khalid ◽  
Nawfal Al-Hadithi ◽  
Dawoude Kaouche
Keyword(s):  
Statistical Analysis ◽  
Statistical Inference ◽  
Real Time ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Google Trends ◽  
Cochrane Library ◽  
Dental Anatomy ◽  
Digital Epidemiology ◽  
The Cochrane Library

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

scholarly journals Real-time Inferential Analytics Based on Online Databases of Trends: A Breakthrough Within the Discipline of Digital Epidemiology of Dentistry and Oral-Maxillofacial Surgery

2019 ◽  
Vol 13 (2) ◽  
pp. 81 ◽  
Author(s):  
Ahmed Al-Imam ◽  
Usama Khalid ◽  
Nawfal Al-Hadithi ◽  
Dawoude Kaouche
Keyword(s):  
Statistical Analysis ◽  
Statistical Inference ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Google Trends ◽  
Cochrane Library ◽  
Review Of The Literature ◽  
Dental Anatomy ◽  
Digital Epidemiology ◽  
The Cochrane Library

Background: Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. Aims and Objectives: We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. Materials and Methods: Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analysis and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. Results: The comprehensive review of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. Conclusion: Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

scholarly journals Alveolar Osteitis Following Tooth Extraction: Systematic Review and Google Trends Analysis Instigated by the First Upper Premolar Case Reported from Iraq

2019 ◽  
Vol 13 (3) ◽  
pp. 1
Author(s):  
Ahmed Al-Imam ◽  
Amer Al-Khazraji
Keyword(s):  
Systematic Review ◽  
Middle East ◽  
Tooth Extraction ◽  
Machine Learning Algorithms ◽  
Google Trends ◽  
Cochrane Library ◽  
Data Repositories ◽  
Painful Condition ◽  
Alveolar Osteitis ◽  
Digital Epidemiology

Introduction： Alveolar osteitis is a painful condition that may occur following permanent teeth extraction due to the failure of formation of a blood clot or its dislodgement before the complete healing of the wound. We aim to provide a systematic review and trends analytic on the epidemiology and the digital epidemiology as well as the management of alveolar osteitis and to seek any available data in connection with alveolar osteitis following upper premolar tooth extraction. Methods： This study represents a combinatory of literature review, analytics of Google Trends, and the first documented case from Iraq of alveolar osteitis following extraction of the maxillary first premolar. Three literature databases were explored, using Boolean operators, including NCBI-PubMed, Elsevier, and the Cochrane Library. Google Trends database was examined to assess the digital epidemiology. Results：The total number of hits was 54417. There was an overall deficit of literature concerning the condition in connection with the extraction of maxillary premolars. The digital epidemiology was limited to twenty-two countries including three countries from the Middle East accounting for 13.63% of the total geographic mapping while Iraq was absent. Conclusion：Our exceptional case report instigated a systematic analytic of a trends database and the literature. The analysis confirmed the inadequacy of studies from the Middle East. Future studies should deploy the use of machine learning algorithms for a rigour statistical inference based on data from online and offline big data repositories of public health records.  

Comparison of conservative treatment with appendectomy for acute uncomplicated pediatric appendicitis: a meta-analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 767-780
Author(s):  
Le-wee Bi ◽  
Bei-lei Yan ◽  
Qian-yu Yang ◽  
Hua-lei Cui
Keyword(s):  
Statistical Analysis ◽  
Treatment Group ◽  
Conservative Treatment ◽  
Success Rate ◽  
Treatment Efficacy ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Significant Difference ◽  
The Cochrane Library

Aim: We aimed to compare conservative treatment with surgery for uncomplicated pediatric appendicitis to estimate effectiveness and safety. Methods: Data recorded until September 2018 were searched, and relevant academic articles from PubMed, EMBASE, the Cochrane Library and other libraries were selected. STATA version 13.0 (Stata Corporation, TX, USA) was used for statistical analysis. Results: We identified nine eligible papers. The study reported a significant difference in the success rate of treatment in 1 month and in 1 year, and no difference in the incidence of complications. The patients with fecaliths showed low treatment efficacy in conservative treatment group (p < 0.05). Conclusion: Standardized conservative treatment as inpatients for pediatric appendicitis is safe and feasible. Appendectomy was the better choice for patients with fecaliths.

scholarly journals Real-time solar image classification: Assessing spectral, pixel-based approaches

2019 ◽  
Vol 9 ◽  
pp. A38
Author(s):  
J. Marcus Hughes ◽  
Vicki W. Hsu ◽  
Daniel B. Seaton ◽  
Hazel M. Bain ◽  
Jonathan M. Darnel ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Random Forest ◽  
Image Classification ◽  
Real Time ◽  
Data Science ◽  
Extreme Ultraviolet ◽  
Machine Learning Algorithms ◽  
The Sun ◽  
Solar Image ◽  
Likelihood Model

In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning algorithms performing solar image classification using Extreme Ultraviolet (EUV) and Hα images: a maximum likelihood model assuming a single normal probability distribution for each theme from Rigler et al. (2012) [Space Weather 10(8): 1–16], a maximum-likelihood model with an underlying Gaussian mixtures distribution, and a random forest model. We create a small database of expert-labeled maps to train and test these algorithms. Due to the ambiguity between the labels created by different experts, a collaborative labeling is used to include all inputs. We find the random forest algorithm performs the best amongst the three algorithms. The advantages of this algorithm are best highlighted in: comparison of outputs to hand-drawn maps; response to short-term variability; and tracking long-term changes on the Sun. Our work indicates that the next generation of solar image classification algorithms would benefit significantly from using spatial structure recognition, compared to only using spectral, pixel-by-pixel brightness distributions.

Evidence of Case Management Effect on Traumatic-Brain-Injured Adults in Rehabilitation

1999 ◽  
Vol 1 (2) ◽  
pp. 87-97 ◽  
Author(s):  
Patricia K. Patterson ◽  
Hugo Maynard ◽  
Randall M. Chesnut ◽  
Nancy Carney ◽  
N. Clay Mann ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Statistical Analysis ◽  
Brain Injury ◽  
Case Management ◽  
Cochrane Library ◽  
Future Research ◽  
Brain Injured ◽  
Research Designs ◽  
The Cochrane Library ◽  
Management Effect

The purpose of this study was to evaluate the evidence for effectiveness of case management during recovery from traumatic brain injury (TBI) in adults. After an overview of TBI incidence, prevalence, and problems, and a brief explanation of case management, the study methods are described, the findings are discussed and recommendations are made for future research. Medline, HealthSTAR, CINAHL, PsychINFO, and the Cochrane Library databases were searched and 83 articles met the criteria for review. The strongest studies (n = 3) were critically appraised and their design features and data were placed in two evidence tables. Due to methodological limitations, there was neither clear evidence of effectiveness nor of ineffectiveness. For future research, we recommend controlled research designs, standardization of measures, adequate statistical analysis and specification of health outcomes of importance to persons with TBI and their families.

Predicting Anticoagulation Need for Otogenic Intracranial Sinus Thrombosis: A Machine Learning Approach

2020 ◽  
Author(s):  
Matthew R. Kaufmann ◽  
Philip Ryan Camilon ◽  
Jessica R. Levi ◽  
Anand K. Devaiah
Keyword(s):  
Machine Learning ◽  
Sinus Thrombosis ◽  
Cerebral Venous Sinus Thrombosis ◽  
Machine Learning Algorithms ◽  
Baseline Risk ◽  
Cochrane Library ◽  
Intracranial Abscess ◽  
Machine Learning Approach ◽  
The Cochrane Library ◽  
Learning Analysis

Abstract Objective The role of anticoagulation (AC) in the management of otogenic cerebral venous sinus thrombosis (OCVST) remains controversial. Our study aims to better define when AC is used in OCVST. Methods MEDLINE, EMBASE, and The Cochrane Library were searched from inception to February 14, 2019 for English and English-translated articles. References cited in publications meeting search criteria were searched. Titles and abstracts were screened and identified in the literature search, assessing baseline risk of bias on extracted data with the methodological index for nonrandomized studies (MINORS) scale. Random effects meta-regression followed by random forest machine learning analysis across 16 moderator variables between AC and nonanticoagulated (NAC) cohorts was conducted. Results A total of 92% of treated patients were free of neurologic symptoms at the last follow-up (mean 29.64 months). Four percent of AC and 14% of NAC patients remained symptomatic (mean 18.72 and 47.10 months). 3.5% of AC patients experienced postoperative wound hematomas. AC and NAC recanalization rates were 81% (34/42) and 63% (five-eights), respectively. OCVST was correlated with cholesteatoma and intracranial abscess. Among the analyzed covariates, intracranial abscess was most predictive of AC and cholesteatoma was most predictive of NAC. Comorbid intracranial abscess and cholesteatoma were predictive of AC. Conclusion The present study is the first to utilize machine learning algorithms in approaching OCVST. Our findings support the therapeutic use of AC in the management of OCVST when complicated by thrombophilia, intracranial abscess, and cholesteatoma. Patients with intracranial abscess and cholesteatoma may benefit from AC and surgery. Patients with cholesteatoma can be managed with NAC and surgery.

scholarly journals Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review

2019 ◽  
Vol 4 (4) ◽  
pp. e001065 ◽  
Author(s):  
Yonatan Moges Mesfin ◽  
Allen Cheng ◽  
Jock Lawrie ◽  
Jim Buttery
Keyword(s):  
Systematic Review ◽  
Statistical Analysis ◽  
Adverse Events ◽  
Signal Detection ◽  
Real Time ◽  
Sequential Analysis ◽  
Vaccine Safety ◽  
Cochrane Library ◽  
Public Confidence ◽  
Healthcare Data

BackgroundConcerns regarding adverse events following vaccination (AEFIs) are a key challenge for public confidence in vaccination. Robust postlicensure vaccine safety monitoring remains critical to detect adverse events, including those not identified in prelicensure studies, and to ensure public safety and public confidence in vaccination. We summarise the literature examined AEFI signal detection using electronic healthcare data, regarding data sources, methodological approach and statistical analysis techniques used.MethodsWe performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Five databases (PubMed/Medline, EMBASE, CINAHL, the Cochrane Library and Web of Science) were searched for studies on AEFIs monitoring published up to 25 September 2017. Studies were appraised for methodological quality, and results were synthesised narratively.ResultWe included 47 articles describing AEFI signal detection using electronic healthcare data. All studies involved linked diagnostic healthcare data, from the emergency department, inpatient and outpatient setting and immunisation records. Statistical analysis methodologies used included non-sequential analysis in 33 studies, group sequential analysis in two studies and 12 studies used continuous sequential analysis. Partially elapsed risk window and data accrual lags were the most cited barriers to monitor AEFIs in near real-time.ConclusionRoutinely collected electronic healthcare data are increasingly used to detect AEFI signals in near real-time. Further research is required to check the utility of non-coded complaints and encounters, such as telephone medical helpline calls, to enhance AEFI signal detection.Trial registration numberCRD42017072741

Efficacy of compression stockings in preventing post-thrombotic syndrome in patients with deep venous thrombosis: a systematic review and metaanalysis

VASA ◽  
2016 ◽  
Vol 45 (2) ◽  
pp. 141-147 ◽  
Author(s):  
Jakob Martin Burgstaller ◽  
Johann Steurer ◽  
Ulrike Held ◽  
Beatrice Amann-Vesti
Keyword(s):  
Systematic Review ◽  
Venous Thrombosis ◽  
Deep Venous Thrombosis ◽  
Cochrane Library ◽  
Compression Stockings ◽  
Post Thrombotic Syndrome ◽  
Study Results ◽  
Preventive Efficacy ◽  
One Year ◽  
The Cochrane Library

Abstract. Background: Here, we update an earlier systematic review on the preventive efficacy of active compression stockings in patients with diagnosed proximal deep venous thrombosis (DVT) by including the results of recently published trials. The aims are to synthesize the results of the original studies, and to identify details to explain heterogeneous results. Methods: We searched the Cochrane Library, PubMed, Scopus, and Medline for original studies that compared the preventive efficacy of active compression stockings with placebo or no compression stockings in patients with diagnosed proximal DVT. Only randomized controlled trials (RCTs) were included. Results: Five eligible RCTs with a total of 1393 patients (sample sizes ranged from 47 to 803 patients) were included. In three RCTs, patients started to wear compression stockings, placebo stockings or no stockings within the first three weeks after the diagnosis of DVT. The results of two RCTs indicate a statistically significant reduction in post-thrombotic syndrome (PTS) of 50% or more after two or more years. The result of one RCT shows no preventive effect of compression stockings at all. Due to the heterogeneity of the study results, we refrained from pooling the results of the RCTs. In a further RCT, randomization to groups with and without compression stockings took place six months after the diagnosis of DVT, and in another RCT, only patients with the absence of PTS one year after the diagnosis of DVT were analyzed. One RCT revealed a significant reduction in symptoms, whereas another RCT failed to show any benefit of using compression stockings. Conclusions: At this time, it does not seem to be justifiable to entirely abandon the recommendations regarding compression stockings to prevent PTS in patients with DVT. There is evidence favoring compression stockings, but there is also evidence showing no benefit of compression stockings.

Barriers and facilitators to conducting research by early career psychiatrists: a literature review

2019 ◽  
Vol 2 (2) ◽  
pp. 135-154
Author(s):  
Katja Koelkebeck ◽  
Maja Pantovic Stefanovic ◽  
Dorota Frydecka ◽  
Claudia Palumbo ◽  
Olivier Andlauer ◽  
...  
Keyword(s):  
Research Participation ◽  
Early Career ◽  
Cochrane Library ◽  
Inclusion Criteria ◽  
Research Career ◽  
Hand Search ◽  
Electronic Search ◽  
Conducting Research ◽  
The Cochrane Library ◽  
To Receive

AbstractObjectivesTo understand and identify factors that promote and prevent research participation among early career psychiatrists (ECPs), in order to understand what would encourage more ECPs to pursue a research career.MethodsWe conducted an electronic search of databases (PubMed and the Cochrane library) using the keywords ‘doctors’, ‘trainees’, ‘residents’, ‘physicians’ and ‘psychiatric trainees’ as well as ‘research’ (MeSH) and ‘publishing’ (MeSH). This search was complemented by a secondary hand search.ResultsWe identified 524 articles, of which 16 fulfilled inclusion criteria for this review. The main barriers included lack of dedicated time for research, lack of mentoring and lack of funding. The main facilitators were opportunities to receive mentorship and access to research funding.ConclusionsAction is needed to counteract the lack of ECPs interested in a career in research. Specific programs encouraging ECPs to pursue research careers and having access to mentors could help increase the current numbers of researching clinicians in the field.

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