Objectives: The purpose of this study was to analyze the relationship between uncontrolled diabetes with periodontal disease (PD) among adults in the US.
Methods: The study sample from the 2009-2014 ...National Health and Nutrition Examination Survey data included 10,071 US participants aged ≥ 30 years in a probability weighted sample. We used the Centers for Disease Control and Prevention/American Academy of Periodontology’s standard to measure PD status by using means of a full-month oral examination at 6 sites per tooth for periodontal probing depth and clinical attachment loss. We used self-reported response to the diabetes questionnaire and laboratory report of HbA1c to assess the participant’s diabetes status (no diabetes, diabetes with HbA1c < 9%, diabetes with HbA1c ≥ 9%). Multiple logistic regression analysis was performed to examine the relationship between diabetes status with PD.
Results: Multiple logistic regression showed that PD was significantly associated with controlled diabetes (adjusted odds ratio (OR) = 1.32, 95% confidence interval (CI): 1.01 - 1.73, p <0.05) and uncontrolled diabetes (OR = 2.48, 95% CI: 1.52 - 4.04, p <0.001) after adjusting for covariates. PD’s protective factors of PD included annual dental visit, female gender, and college education. PD’s risk factors were smoking status, racial minorities, income <200% Federal Poverty Guidelines, and older age.
Conclusions: Uncontrolled diabetes was significantly associated with a higher risk of periodontal disease among adults in the US. Physicians should work with dentists to strongly recommend patients with diabetes to check their glycemic level and have an annual dental visit.
Disclosure
G.Vu: None. B.B.Little: None. C.King: None. V.P.Gurupur: None. S.Shakib: None.
The article is a narrative review that briefly describes some of the recent advances in healthcare data management that will have positive effect on mHealth. The advances described in this article ...are in fact innovation introduced by the author to the field of data management with respect to electronic health records. The research delineated is transdisciplinary in nature and will potentially have positive impact on healthcare outcomes. Also, the article illustrates the necessity for an out of the box thinking approach to improve mHealth while discussing the current impending issues related to data incompleteness of electronic health records and the much-needed decision support systems for mHealth. It is to be noted that most of the electronic health records are now accessed by patients through mobile devices. These mobile devices will run as clients while much of the heavy computing is performed using servers. Here it is important to discuss some of the important technologies and methods used for decision making. The article attempts to present a discussion on how this myriad of intertwining technologies support this decision making with respect to electronic health records. More importantly it is these processes that assist in decision making and efficiency for both mHealth users and providers. In this respect, the article first provides insights on the complexities of decision making involved with electronic health records. This is followed by a discussion on the problem of data incompleteness of electronic health records. Finally, the author provides some insights into the gravity of the problem of data incompleteness in terms of revenue loss/gain for healthcare providers.
The purpose of this article is to propose a methodology involving various methods that can be used to predict the data incompleteness of a dataset. Here the investigators have presented data ...incompleteness as both continuous and discrete random variables. In addition the investigators used transfer entropy for the purpose of advancing the science associated with the analysis of data incompleteness of electronic health records. The underlying methodology has been coined as "Machine Learning Analysis for Data Incompleteness" (MADI) with the intention of developing a possible solution to data incompleteness in electronic health records. MADI advances the analysis of data incompleteness with the use of Kolomogorov Smirnov goodness of fit, mielke distribution, and beta distributions for a holistic analysis. Alongside the methodology presented, the investigators explored stochastic gradient descent, generalized additive models, and support vector machines for comparison. Overall, the investigators have presented a complete set of methods and algorithms to help predict data incompleteness in a medical setting and provided suggestions for practical applications into the prediction of data incompleteness.
The purpose of this article is to perform a scientific analysis of the definitions associated with healthcare informatics and healthcare data analytics. Additionally, the authors attempt to redefine ...the scientific pursuit of healthcare informatics and healthcare data analytics. This commentary can assist the thinking of informaticians and data analysts working in healthcare management and practice. The authors also provide a brief insight on the possible future direction of informatics and analytics associated with healthcare.
Machine Learning, a branch of Artificial Intelligence, is influencing society, industry and academia at large. The adaptability of Python programming language to Machine Learning has increased its ...popularity further. Another technology on the horizon is the Internet of Things (IoT). This book tries to address IoT, Python and Machine Learning along with a brief introduction to Image Processing. If you are a novice programmer or have just started exploring the IoT or Machine Learning with Python, this book is for you.
Features:
Raspberry Pi as IoT is described along with procedures for installation and configuration.
A simple introduction to Python Programming Language, along with its popular library packages like NumPy, Pandas, SciPy and Matplotlib, are explained exhaustively along with relevant examples.
Machine Learning with Python Scikit-Learn library is explained with an emphasis on supervised learning and classification.
Image processing on IoT is introduced for an audience who loves to apply Machine Learning algorithms to Images.
This book follows a hands-on approach and provides a huge collection of Python programs.
About the Authors
Dr. Shrirang Ambaji Kulkarni is a prolific learner, author and faculty member with over 18 years of experience in the field of Computer Science and Engineering. He currently works as an Associate Professor at the National Institute of Engineering in the Department of Computer Science and Engineering, Mysore, India.
Dr. Varadraj Gurpur is an eminent faculty member and researcher in the field of Health Informatics. He has authored many research articles in reputed journals. He currently works as an Associate Professor at the University of Central Florida in the Department of Health Informatics, Florida, USA.
Dr. Steven Lawrence Fernandes is a high-profile researcher in the field of Image Processing. He currently works as a Post-Doctoral Fellow at the University of Central Florida in the Department of Computer Science and Engineering, Florida, USA.
Available literature clearly indicates that successful implementation of telemedicine and telehealth has been a challenge. This challenge is further amplified if the reader must consider this ...implementation in a rural setting. In this article the authors discuss some of the key challenges associated with this implementation. The article sheds light on a few key studies and commentaries associated with the use of telehealth in a rural setting. Critically, the article summarizes these critical findings; thereby, informing the reader on the bottlenecks associated with the use of telehealth in a geographically rural area. Also, briefly summarizing the existing body of knowledge on this topic of study. Furthermore, a case study briefly narrating the use of telemedicine and telehealth for rural Oklahoma is presented to advance our understanding of the situation in this field. Some of the critical details associated with this case study provides insights on some of the key challenges associated with the implementation of telehealth in a rural setting. This case study also provides insights on key workflow processes that helped the implementation of telehealth. Finally, the authors summarize the key challenges in the implementation of telehealth based on their perspective. Here it is important to inform the readers that this article is not a scientific review on the topic instead presents an opinion backed by facts and existing literature. Overall, the authors present a key discussion that can lead to advances in research and required innovations that might help in providing easy access to healthcare through telehealth.
Abstract
This study examined the relationship between uncontrolled diabetes and periodontal disease (PD) among adults in the United States. We used data from the 2009–2014 National Health and ...Nutrition Examination Survey (NHANES) with a sample of 6108 adults ages 30 and over. To measure PD status, we used the Centers for Disease Control and Prevention/American Academy of Periodontology’s standards. To classify DM status (no DM, DM with HbA
1c
< 9%, diabetes with HbA
1c
≥ 9%),we used self-reported Diabetes Mellitus (DM) diagnosis and laboratory report of HbA
1c
. Approximately 8.5% of the sample had controlled DM, and 1.7% had uncontrolled DM, for a total of 10.2% DM in the analysis. Multivariate logistic regression showed that compared to those without DM, PD was significantly increased with controlled DM (adjusted odds ratio (aOR) = 1.32, 95% confidence interval (CI) 1.01–1.73, p < 0.05) and even more with uncontrolled DM (aOR = 2.48, 95% CI 1.52–4.04, p < 0.001), after adjusting for covariates. Factors that reduced the prevalence of PD included annual dental visits, female gender, and college education. Factors that significantly increased PD prevalence were cigarette smoking, non-white race, income < 200% Federal Poverty Level, and older age (age > 50 years). In conclusion, uncontrolled DM was significantly associated with higher odds of PD among adults in the US.
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The purpose of this article is to illustrate an investigation of methods that can be effectively used to predict the data incompleteness of a dataset. Here, the investigators have conceptualized data ...incompleteness as a random variable, with the overall goal behind experimentation providing a 360-degree view of this concept conceptualizing incompleteness of a dataset both as a continuous, discrete random variable depending on the aspect of the required analysis. During the course of the experiments, the investigators have identified Kolomogorov–Smirnov goodness of fit, Mielke distribution, and beta distributions as key methods to analyze the incompleteness of a dataset for the datasets used for experimentation. A comparison of these methods with a mixture density network was also performed. Overall, the investigators have provided key insights into the use of methods and algorithms that can be used to predict data incompleteness and have provided a pathway for further explorations and prediction of data incompleteness.
The objective of this article is to discuss the inherent bias involved with artificial intelligence-based decision support systems for healthcare. In this article, the authors describe some relevant ...work published in this area. A proposed overview of solutions is also presented. The authors believe that the information presented in this article will enhance the readers' understanding of this inherent bias and add to the discussion on this topic. Finally, the authors discuss an overview of the need to implement transdisciplinary solutions that can be used to mitigate this bias.
Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many ...Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical formulations. In this study, we have first developed a DLT to programme two types of deep learning neural networks, namely: (a) a two-hidden layer network, and (b) a three-hidden layer network. The data was analysed for predictability in both of these networks. Additionally, a comparison was also made with simple and multiple Linear Regression (LR). The demonstration of successful application of this method is carried out using the dataset that was constructed based on 2014 Medicare Provider Utilization and Payment Data. The results indicate a stronger case to use DLTs compared to traditional techniques like LR. Furthermore, it was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study. Therefore, the experimentation described in this article sets up a case for using DLTs over the traditional predictive analytics. The investigators assume that the algorithms described for deep learning is repeatable and can be applied for other types of predictive analysis on healthcare data. The observed results indicate, the accuracy obtained by DLT was 40% more accurate than the traditional multivariate LR analysis.
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