Influence Of Diabetic Kidney And Eye Complications On Annual Health Expenditures Using The Medical Expenditures Panel Survey 2012-2016

Abstract Background Diabetes is a critical chronic disease that has exerted considerable health and economic influence on the United States population. Although diabetes has imposed a considerable economic burden on health systems worldwide, the incremental economic effect of certain diabetic complications has not been extensively studied and quantified. The aim of this study is to quantitatively define the economic burden resulting from the presence of diabetic eye and kidney complications. Methods Data was analyzed from the Medical Expenditure Panel Survey (MEPS) 2012-2016 to provide an updated evaluation on the association between several forms of annual health expenditures and the presence of diabetic kidney and eye complications. Demographic qualities of the U.S. diabetic population were analyzed by presenting weighted percentages according to complication status. Total expenditures and several component sources were analyzed by presenting mean and standard error of spending. A multivariable regression was used to evaluate effects from comorbidities and complications on total, out of pocket, and prescription-based annual spending. Out of pocket and prescription spending were chosen as supplement models to characterize the individual patient burden. Results Diabetic complications were more common among the elderly, Non-Hispanic Black and Hispanic individuals, and those with related comorbid diseases. Increased spending due to the presence of these diabetic complications was generally observed in trends of total annual expenditures as well as several component sources of spending. Total annual spending increased by 74% among those with kidney complications, while the presence of eye complications was associated with a 33% increase. Diabetic kidney complications were associated with a 54% increase in annual out of pocket spending, while eye complications led to a 15% increase. Annual prescription spending was elevated by 66% in those with kidney complications, and increased by 36% among patients with diabetic eye complications. Conclusions This analysis clearly illustrates that the economic difficulty patients with diabetic complications requires targeted interventions. It is also demonstrated that these measures are likely to benefit those who are older and possess other related chronic comorbidities. The presence of diabetic kidney and eye complications generally led to increases among many components of health spending, often with the most considerable elevation among those with both conditions. More specifically, notable increases in out of pocket and prescription expenditures suggest that alleviation of the personal financial difficulty may be most critical when implementing reform to benefit these patients

A NEW APPROACH FOR CONFLICT RESOLUTION AND RULE PROCESSING IN A KNOWLEDGE-BASED SYSTEM

In a knowledge-based system, rules can be defined to derive virtual attributes. Conflicts occur if multiple rules are applicable and one must be selected based on some criterion, such as priority. We identify important properties of a conflict resolution method and describe a technique for resolving conflicts and efficiently processing queries involving virtual attributes in a knowledge-based system. It is shown that by transforming a given, prioritized set of rules into a conflict-free, priority independent form it is possible to do query processing in a set-at-a-time manner. Algorithms for conflict resolution and query processing are given

USING MACHINE LEARNING TO OPTIMIZE PREDICTIVE MODELS USED FOR BIG DATA ANALYTICS IN VARIOUS SPORTS EVENTS

In today’s world, data is growing in huge volume and type day by day. Historical data can hence be leveraged to predict the likelihood of the events which are to occur in the future. This process of using statistical or any other form of data to predict future outcomes is commonly termed as predictive modelling. Predictive modelling is becoming more and more important and is trending because of several reasons. But mainly, it enables businesses or individual users to gain accurate insights and allows to decide suitable actions for a profitable outcome. Machine learning techniques are generally used in order to build these predictive models. Examples of machine learning models ranges from time-series-based regression models which can be used for predicting volume of airline related traffic and linear regression-based models which can be used for predicting fuel efficiency. There are many domains which can gain competitive advantage by using predictive modelling with machine learning. Few of these domains include, but are not limited to, banking and financial services, retail, insurance, fraud detection, stock market analysis, sentimental analysis etc. In this research project, predictive analysis is used for the sports domain. It’s an upcoming domain where machine learning can help make better predictions. There are numerous sports events happening around the globe every day and the data gathered from these events can very well be used for predicting as well as improving the future events. In this project, machine learning with statistics would be used to perform quantitative and predictive analysis of dataset related to soccer. Comparisons of these models to see how effectively the models are is also presented. Also, few big data tools and techniques are used in order to optimize these predictive models and increase their accuracy to over 90%

HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces

Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necessary to practically solve the problem of nearest neighbor search. In this paper, we propose a novel yet simple indexing scheme, HD-Index, to solve the problem of approximate k-nearest neighbor queries in massive high-dimensional databases. HD-Index consists of a set of novel hierarchical structures called RDB-trees built on Hilbert keys of database objects. The leaves of the RDB-trees store distances of database objects to reference objects, thereby allowing efficient pruning using distance filters. In addition to triangular inequality, we also use Ptolemaic inequality to produce better lower bounds. Experiments on massive (up to billion scale) high-dimensional (up to 1000+) datasets show that HD-Index is effective, efficient, and scalable.Comment: PVLDB 11(8):906-919, 201

Micromachining of Single Cell Array for Oxygen Consumption Rate Analysis

The Oxygen Consumption Rate of biological cells is an important parameter of cellular metabolism. In order to study the behaviour of cell populations, it becomes necessary to capture and store them in one location for analysis. Individual cell analysis within a cell group can provide useful information about the average response of the cell group, as well as identify outliers. Such analysis can be used to identify different groups of cells based on their oxygen levels. However, characterizing the individual cell response within a cell group is challenging since cell dimensions are on the order of a few micrometers. Conventional techniques, such as microtiter plates and flow cytometry, are unable to offer both the high temporal and the high spatial resolution that is required to characterize individual cells. Modern micromachining and microfabrication techniques, on the other hand, allow for the creation of devices that have dimensions that are on the order of a few micrometers. Through a series of thin film deposition, photolithography and thin film etching techniques, it is possible to create single cell trapping structures whose dimensions are only slightly larger than that of individual cells. The aim of this thesis is to create a process flow in order to fabricate such structures on a single crystalline silicon substrate using available micromachining techniques

Micromachining of Single Cell Array for Oxygen Consumption Rate Analysis

The Oxygen Consumption Rate of biological cells is an important parameter of cellular metabolism. In order to study the behaviour of cell populations, it becomes necessary to capture and store them in one location for analysis. Individual cell analysis within a cell group can provide useful information about the average response of the cell group, as well as identify outliers. Such analysis can be used to identify different groups of cells based on their oxygen levels. However, characterizing the individual cell response within a cell group is challenging since cell dimensions are on the order of a few micrometers. Conventional techniques, such as microtiter plates and flow cytometry, are unable to offer both the high temporal and the high spatial resolution that is required to characterize individual cells. Modern micromachining and microfabrication techniques, on the other hand, allow for the creation of devices that have dimensions that are on the order of a few micrometers. Through a series of thin film deposition, photolithography and thin film etching techniques, it is possible to create single cell trapping structures whose dimensions are only slightly larger than that of individual cells. The aim of this thesis is to create a process flow in order to fabricate such structures on a single crystalline silicon substrate using available micromachining techniques

Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal component