Healthcare Data Analytics on the Cloud

Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution

Analysis of Research in Healthcare Data Analytics

The main aim of this paper is to provide a deep analysis on the research field of healthcare data analytics., as well as highlighting some of guidelines and gaps in previous studies. This study has focused on searching relevant papers about healthcare analytics by searching in seven popular databases such as google scholar and springer using specific keywords, in order to understand the healthcare topic and conduct our literature review. The paper has listed some data analytics tools and techniques that have been used to improve healthcare performance in many areas such as: medical operations, reports, decision making, and prediction and prevention system. Moreover, the systematic review has showed an interesting demographic of fields of publication, research approaches, as well as outlined some of the possible reasons and issues associated with healthcare data analytics, based on geographical distribution theme

Healthcare Data Analytics and Privacy Preservation by DCNN Algorithm

Data has become an integral part of the digital world with the advancement in computing technologies. The collection of data is very crucial with regards to data analytics. Every industry makes use of data analytics ranging from financial to other commercial applications but it becomes even more important in healthcare domain for the analysis of healthcare data. The present research work is mainly focused on classification/prediction problems of healthcare data based on deep learning (supervised) approaches using data mining techniques. There is a need to design an intelligent model (based on deep learning) which can classify the amount of data that is stored in our databases. Human data analytical capability rate is much smaller when compared to the amount of data that is stored. This (classification) becomes even more critical when it comes to healthcare data as it can help to detect, diagnose and treat the patients based on these classified data. The main goal of the thesis is to develop a deep learning-based model for classification tasks and the introduced DDS can be used in healthcare domain to improve the diagnostic speed, accuracy and reliability

Healthcare data analytics for social good

Healthcare problems, ranging from soaring medical costs to the COVID-19 pandemic, present major challenges to our society. Better solutions to these problems can potentially improve the lives and livelihood of tens of millions of people. This thesis consists of three essays on using healthcare data analytics to address pressing social challenges. Specifically, the first two essays focus on evaluation and improvement of risk adjustment designs in healthcare capitation programs, while the third essay develops a machine learning algorithm to detect county-level COVID-19 outbreaks.Ph.D

Reconsidering Bipolar Scales Data As Compositional Data Improves Psychometric Healthcare Data Analytics

Correct psychometric profiling and the choice of adequate therapeutic measures are the basis of any psychotherapeutic treatment. The preparation of a correct psychological profile benefits the patient and saves time and costs. Regarding psychometric questionnaires it is common practice to consider data of bipolar scales as interval scaled. This paper reveals the true compositional data structure (namely the Simplex) with respect to the psychometric limit of quantification of bipolar traits and constructs. The Simplex heavily affects the set of statistical procedures applicable. Disregarding the Simplex causes serious bias and results in erroneous standards and standard deviations, biased correlations, reduced convergent validity and a loss of statistical power. In this paper, the isometric log-ratio (ilr) transformation is suggested. It transforms Simplex data towards the interval scale and provides unbiased results, e.g., standards. By means of a simulation study, this paper shows that up to an 18\% increase in the statistical power of the well-known correlation test based on Student's t-distribution can be achieved. As the statistical power increases the sample size of psychometric studies can be reduced resulting in lower data collection costs. Besides economic and psychotherapeutic aspects, the results of the simulation study generalize from correlation analysis towards a larger set of standard statistical procedures. For example, testing the hypothesis of equality of the two means of independent samples using a t-test based on Student's t distribution is equivalent to testing the hypothesis of a null-correlation between the binary grouping variable and the dependent variable. Furthermore, the coefficient of correlation contributes to the slope of a regression line. Thus, the ilr approach also affects linear regression techniques

Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach

In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF, which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases

Computational Intelligence Based Electronic Healthcare Data Analytics Using Feature Selection with Classification by Deep Learning Architecture

EHRs (Electronic health records) are a source of big data that offer a wealth of clinical patient health data. However, because these notes are free-form texts, writing formats and styles range greatly amongst various records, text data from eHRs, such as discharge rapid notes, provide analysis challenges. This research proposed novel technique in electronic healthcare data analysis based on feature selection and classification utilizingDL methods. here the input is collected as input EH data, is processed for dimensionality reduction, noise removal. A public data pre-processing method for dealing with HD-EHR data is dimensionality reduction, which tries to minimize amount of EHR representational features while enhancing effectiveness of following data analysis, such as classification. The processed data features has been selected utilizingweighted curvature based feature selection with support vector machine. Then this selected deep features has been classified using sparse encoder transfer learning. the experimental analysis has been carried out for various EH datasets in terms of accuracy of 96%, precision of 92%, recall of 77%, F-1 score of 72%, MAP of 65