Predictive Analytics Approach Research Articles

Evaluating Patient Readmission Risk: A Predictive Analytics Approach

With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting. Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions. Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.

A predictive analytic approach to determine construction cost estimates

A predictive analytic approach to determine construction cost estimates

A predictive analytic approach to determine construction cost estimates

In the construction industry, cost estimates are the basis for which projects are awarded to suppliers. A cost estimate is arguably the most important inclusion in a supplier's response to a customer's 'request for quote'. As such, it is essential for construction cost estimates to include features of accuracy, cost effectiveness and profitability for the supplier. The amount of variables and qualitative considerations in an estimate require a methodology beyond an objective approach. This paper uses data analytics to analyse both a US Government service provider's internal estimate process and suppliers' external estimate process to determine the best approach to use on future task orders. Three different classifier methods were used to perform the data analytic approach. Based on analyses performed for all three methods, RBF-based support vector machine with a 66%–33% random split and ten-fold cross-validation yielded the highest accuracy at 71%.

Rodent ultrasonic vocalizations as biomarkers of future alcohol use: A predictive analytic approach.

Excessive alcohol consumption has a vast, negative impact on society. Rodent models have been successful in furthering our understanding of the biological underpinnings that drive alcohol consumption. Rodents emit ultrasonic vocalizations (USVs) that are each composed of several acoustic characteristics (e.g., frequency, duration, bandwidth, power). USVs reflect neurotransmitter activity in the ascending limb of the mesolimbic dopaminergic and cholinergic neurotransmitter systems and serve as noninvasive, real-time biomarkers of dopaminergic and cholinergic neurotransmission in the limbic system. In the present study, we recorded spontaneously emitted USVs from alcohol-naïve Long-Evans (LE) rats and then measured their alcohol intake. We compared the USV acoustic characteristics and alcohol consumption data from these LE rats with previously published data from selectively bred high-alcohol (P and HAD-1) and low-alcohol (NP and LAD-1) drinking lines from studies with the same experimental method. Predictive analytic techniques were applied simultaneously to this combined data set and revealed that (a) USVs emitted by alcohol-naïve rats accurately discriminated among high-alcohol consuming, LE, and low-alcohol consuming rat lines, and (b) future alcohol consumption in these same rat lines was reliably predicted from the USV data collected in an alcohol-naïve state. To our knowledge, this is the first study to show that alcohol consumption is predicted directly from USV profiles of alcohol-naïve rats. Because USV acoustic characteristics are sensitive to underlying neural activity, these findings suggest that baseline differences in mesolimbic cholinergic and dopaminergic tone could determine the propensity for future alcohol consumption in rodents.

PREDICTIVE ANALYTICS CONCEPTS IN BIG DATA- A SURVEY

Nowadays the increase of data variety considered very dispute problem for analysis. So innovative methods are mandatory for analytics especially in big data where the data in characteristic very complex and unstructured. The analytics is the process of analysis to predict concealed pattern and association among data. The main objective of this survey paper is to provide the exhaustive view of different predictive analytics applications and approaches. Analytics methods focused with dissimilar perspectives based on applications and data variety. Some of the application discussed is big data in hotel governance, higher education, health care, data e-governance, consumer orientations. This paper present different predictive approaches adapted for different application with challenges and suggestions.

Making sense of learner and learning Big Data: reviewing five years of Data Wrangling at the Open University UK

ABSTRACTMost distance learning institutions collect vast amounts of learning data. Making sense of this ‘Big Data’ can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated in 2012 a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical and sense-making advice to staff and senior management. Given substantial changes within the OU (e.g. new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case study provides an in-depth review of lessons learned of five years of data wrangling. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.

Automated robot-assisted surgical skill evaluation: Predictive analytics approach.

Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise. Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied. The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task. This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.

Modeling and predicting weather in agro-climatic scarcity zone using iterative approach

Weather predictions could be used to give decision support guidelines for the agricultural management. The agricultural yield productivity depends on crop protection and its effective management, which could be increased by avoiding losses due to the effect of low and high-temperature damage. In Maharashtra state, the seasonal changes affect potential losses of susceptible crops and livestocks, due to variations in temperature. Therefore, it is essential to give effective decisions which are used to avoid damages from the extreme weather conditions. The main goal of the study is to give predictive weather analytics in agro-climatic scarcity region using the iterative approach. In this study, predictive weather analytics approach is proposed based on iterative linear regression and polynomial regression predicting methods. The study region of research falls in the agro-climatic scarcity region having inadequate rainfall, variation in temperature, and dry land. So, the proposed iterative approach based on linear methods are applied and designed to predict future conditions. Both models provide findings that are useful for the future farming and management. Also, comparative study and plots are depicted for the actual and estimated maximum and minimum values of temperature, humidity, and rainfall results of the proposed approach. The effectiveness and performance are tested using the statistical tests and error measure statistics. The prediction accuracy of the maximum and minimum temperature, humidity, and rainfall at the 95% confidence level is tested. The estimates using iterative polynomial approach is much better compared to iterative linear regression approach. The statistical measures and error estimators, such as mean, variance, median, standard deviation, kurtosis, mean squared error, root-mean-squared error and correlation coefficient are evaluated and discussed in detail. Lastly, the statistical hypothesis testing methods such as ANOVA, t test, f test and z test are used in this study to test the actual and predicted data of the model. Also, the frost conditions and occurrence probability in the estimated year 2013 (20% in the month May and 38.33% in December–January) are noted. The results of proposed approach demonstrate the visualization, accuracy, and suitability for the managing decisions in the agricultural products and livestock.

A data-driven strategy for predicting greenness scores, rationally comparing synthetic routes and benchmarking PMI outcomes for the synthesis of molecules in the pharmaceutical industry

A predictive analytics approach to understanding process mass intensity (PMI) is described. This method leverages real-world data to predict probable PMI outcomes for a potential synthetic route and to compare PMI outcomes to the summation of prior experience.

Technology-Based Early Warning Systems for Bipolar Disorder: A Conceptual Framework.

Recognition and timely action around “warning signs” of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that early warning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for early warning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating early warning systems for bipolar disorder with a focus on pragmatic considerations.

Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors

This research highlights a contextual application for big data within a HR case study setting. This is achieved through the development of a normative conceptual model that seeks to envelop employee behaviors and attitudes in the context of organizational change readiness. This empirical application considers a data sample from a large public sector organization and through applying Structural Equation Modelling (SEM) identifies salary, job promotion, organizational loyalty and organizational identity influences on employee job satisfaction (suggesting and mediating employee readiness for organizational change). However in considering this specific context, the authors highlight how, where and why such a normative approach to employee factors may be limited and thus, proposes through a framework which brings together big data principles, implementation approaches and management commitment requirements can be applied and harnessed more effectively in order to assess employee attitudes and behaviors as part of wider HR predictive analytics (HRPA) approaches. The researchers conclude with a discussion on these research elements and a set of practical, conceptual and management implications of the findings along with recommendations for future research in the area.

Forecasting smog-related health hazard based on social media and physical sensor

Smog disasters are becoming more and more frequent and may cause severe consequences on the environment and public health, especially in urban areas. Social media as a real-time urban data source has become an increasingly effective channel to observe people׳s reactions on smog-related health hazard. It can be used to capture possible smog-related public health disasters in its early stage. We then propose a predictive analytic approach that utilizes both social media and physical sensor data to forecast the next day smog-related health hazard. First, we model smog-related health hazards and smog severity through mining raw microblogging text and network information diffusion data. Second, we developed an artificial neural network (ANN)-based model to forecast smog-related health hazard with the current health hazard and smog severity observations. We evaluate the performance of the approach with other alternative machine learning methods. To the best of our knowledge, we are the first to integrate social media and physical sensor data for smog-related health hazard forecasting. The empirical findings can help researchers to better understand the non-linear relationships between the current smog observations and the next day health hazard. In addition, this forecasting approach can provide decision support for smog-related health hazard management through functions like early warning.

Predicting online product sales via online reviews, sentiments, and promotion strategies

Purpose– The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.Design/methodology/approach– The authors designed a big data architecture and deployed Node.js agents for scraping theAmazon.compages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.Findings– This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.Originality/value– This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

Demographics, policy, and foster care rates; A Predictive Analytics Approach

Individual, family, and community-level factors have been suggested as explanations of foster care entry rates and average lengths of time that children remain in foster care. They do not, however, provide a sufficient explanation of the substantial geographical variation in entry rates and average lengths of stay across the United States. State-level child welfare policies and state-level socioeconomic variables may help explain these trends, but no empirical analysis to date has identified how policies and socioeconomic facts might interact in ways that can help account for the wide geographic differences.Traditional statistical methods and much prior research have been unable to identify the combinatorial and nonlinear interaction effects of the many suggested factors. A data set of 104 state-level variables was constructed to help answer the question of what accounts for geographic differences in foster care entry rates and average lengths of stay in foster care. A predictive analytics approach (classification and regression trees) was used to sort through all the potential explanatory variables, their interactions, and combinations. The results show that state cultural orientations and socioeconomic facts together best explain foster care entry rates. In contrast, child welfare policy and practice differences together best explain average lengths of stay in foster care. Interventions aimed at goals relating to who goes into foster care and how many children go into foster care might be most effective if they focus on culture and socioeconomic facts. Interventions aimed to change lengths of time in care, on the other hand, might be most effective if targeted at state child welfare policies and practices.

An SEM–artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline

There is a dearth of studies pertaining to the influence of SERVPERF on customer satisfaction and customer loyalty among low cost and full service airlines. Prior studies have measured service quality using the GAP-5 model with SERVQUAL; however this study offers a new perspective by using the SERVPERF with an SEM–artificial-neural-networks predictive analytic approach. This is different from the previous studies as it contributes to application of expert systems and intelligent algorithms in the context of low cost and full service airline. The findings revealed significant influences of SERVPERF dimensions on customer satisfaction towards customer loyalty with 63.1% and 55.6% variance explained. The implications from this research may help CEOs and managers of the air travel and tourism industry players to make better decisions in their resource planning stage, at the same time improving customer satisfaction and loyalty.

A predictive analytics approach for demand forecasting in the process industry

AbstractAnticipating demand changes is critical in the process industry with high capacity utilization. The developed solution for demand forecasting is based on a new predictive analytics approach and sophisticated information technology, which allows combining company data and economic information specifically matched to the market environment of product segments. The approach systematically selects the best information for a business outlook. Using the data from the global chemical company BASF, the first empirical results show that this approach significantly outperforms statistical methods based on historical demand data only.

A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD.

Hospital readmission has become a critical metric of quality and cost of healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20% of patients who are readmitted within 30days of discharge. Although several interventions such as transition care management have been practiced in recent years, the effectiveness and sustainability depends on how well they can identify patients at high risk of rehospitalization. Based on the literature, most current risk prediction models fail to reach an acceptable accuracy level; none of them considers patient's history of readmission and impacts of patient attribute changes over time; and they often do not discriminate between planned and unnecessary readmissions. Tackling such drawbacks, we develop a new readmission metric based on administrative data that can identify potentially avoidable readmissions from all other types of readmission. We further propose a tree-based classification method to estimate the predicted probability of readmission that can directly incorporate patient's history of readmission and risk factors changes over time. The proposed methods are validated with 2011-12 Veterans Health Administration data from inpatients hospitalized for heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease in the State of Michigan. Results shows improved discrimination power compared to the literature (c-statistics >80%) and good calibration.

Dynamic AI-Geo Health Application based on BIGIS-DSS Approach

Public health organizations use GIS on a daily basis for input, storage, manipulation, and output of geographic information which provides a powerful tool for public health assessment and monitoring in remote locations and developing countries (1). Information technology is taking on a new level of importance due to its business intelligence application solutions. Our proposal is aimed at integrating these two different worlds in a single dynamic framework using web technology at health sector. Dynamic AI-Geohealth application based on BIGIS_DSS approach uses GIS tools and techniques to convert geographic information and analysis of data that becomes valuable information to make more informed business decisions and rational. Thus, the approach is not just a technical or business analytical process; But discipline which includes both. This paper explores the adopting Business Intelligence (BI) with Geographic Information System (GIS) to build a spatial artificial intelligence and predictive analytical approach. Also this paper will explores case research for implementation of proposed approach in developing countries like Egypt at health sector as dynamic health map web application. The proposed approach helps in solving spatial problem which faces decision makers at health

A Proposed Integrated Approach for BI and GIS in Health Sector to Support Decision Makers (BIGIS-DSS)

This paper explores the possibilities of adopting Business Intelligence (BI), and Geographic Information System (GIS) to build a spatial intelligence and predictive analytical approach. The proposed approach will help in solving spatial problem which faces decision makers at health sector. The proposed spatial analytical approach will cover three main health planning issues. These issues are tackling health inequalities through geospatial monitor for inequalities in distribution of health units and its services, support decision-making with predictive analytics for common health indicators, and geoprocessing for input layers through dynamic health map and motion charts to support decision making.

Exploration for Understanding in Cognitive Modeling

The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments – may achieve an improved understanding of architectures and models – if there is more emphasis on systematic sensitivity and necessity analyses during model development, evaluation, and comparison. We demonstrate this methodological prescription with two of the models submitted for the Dynamic Stocks and Flows (DSF) Model Comparison Challenge, exploring the complex interactions among architectural mechanisms, knowledge-level strategy variants, and task conditions. To cope with the computational demands of these analyses we use a predictive analytics approach similar to regression trees, combined with parallelization on high performance computing clusters, to enable large scale, simultaneous search and exploration.