Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. In this study, we use three categories of medical check-up data, laboratory tests, self-reported medical history, and self-reported health behavior data to build high-cost user prediction models, and to assess the medical check-up features as predictors of high-cost users. Using three data-mining models, logistic regression, random forest, and neural network models, we show that under the diagnosis-based approach, medical check-up data marginally improve diagnosis-based prediction models. Under the cost-based approach, we find that medical check-up data improve cost-based prediction models marginally and medical check-up data can be a viable alternate data source to diagnosis data in predicting high-cost users.

Weltweit wird das Leben der Menschen zunehmend und tiefgreifend von großen Datenmengen, Big Data genannt, beeinflusst. Sie erfassen weitgehend alle Lebensbereiche, selbst wenn dies dem Einzelnen nicht immer bewusst ist.

Weltweit wird das Leben der Menschen zunehmend und tiefgreifend von großen Datenmengen, Big Data genannt, beeinflusst. Sie erfassen weitgehend alle Lebensbereiche, selbst wenn dies dem Einzelnen nicht immer bewusst ist.

Prescriptive analytics extends on predictive analytics by allowing to estimate an outcome in function of control variables, allowing as such to establish the required level of control variables for realizing a desired outcome. Uplift modeling is at the heart of prescriptive analytics and aims at estimating the net difference in an outcome resulting from a specific action or treatment that is applied. In this article, a structured and detailed literature survey on uplift modeling is provided by identifying and contrasting various groups of approaches. In addition, evaluation metrics for assessing the performance of uplift models are reviewed. An experimental evaluation on four real-world data sets provides further insight into their use. Uplift random forests are found to be consistently among the best performing techniques in terms of the Qini and Gini measures, although considerable variability in performance across the various data sets of the experiments is observed. In addition, uplift models are frequently observed to be unstable and display a strong variability in terms of performance across different folds in the cross-validation experimental setup. This potentially threatens their actual use for business applications. Moreover, it is found that the available evaluation metrics do not provide an intuitively understandable indication of the actual use and performance of a model. Specifically, existing evaluation metrics do not facilitate a comparison of uplift models and predictive models and evaluate performance either at an arbitrary cutoff or over the full spectrum of potential cutoffs. In conclusion, we highlight the instability of uplift models and the need for an application-oriented approach to assess uplift models as prime topics for further research.

In den letzten Jahrzehnten fand ein dramatischer Wandel im Bereich der Volkswirtschaftslehre weg von einer uberwiegend theoretisch gepragten hin zu einer uberwiegend empirischen Disziplin statt. So zeigt eine von Hamermesh (2013) durchgefuhrte Analyse der Publikationen in drei fuhrenden okonomischen Zeitschriften1, dass im Jahr 1983 knapp 62% aller Publikationen in diesen Zeitschriften rein theoretischer und lediglich knapp uber 38% empirischer Natur waren. Dabei verwendeten 35% der publizierten Beitrage Sekundardaten, 2,4% eigens erhobene Daten und bei lediglich 0,8% der Beitrage handelte es sich um experimentelle Studien.

Storytelling connects entities (people, organizations) using their observed relationships to establish meaningful storylines. This can be extended to spatiotemporal storytelling that incorporates locations, time, and graph computations to enhance coherence and meaning. But when performed sequentially these computations become a bottleneck because the massive number of entities make space and time complexity untenable. This article presents DISCRN, or distributed spatiotemporal ConceptSearch-based storytelling, a distributed framework for performing spatiotemporal storytelling. The framework extracts entities from microblogs and event data, and links these entities using a novel ConceptSearch to derive storylines in a distributed fashion utilizing key-value pair paradigm. Performing these operations at scale allows deeper and broader analysis of storylines. The novel parallelization techniques speed up the generation and filtering of storylines on massive datasets. Experiments with microblog posts such as Twitter data and Global Database of Events, Language, and Tone events show the efficiency of the techniques in DISCRN.

"Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for discrimination-aware data mining and fairness-aware data mining aim at keeping decision processes supported by information technology free from unjust grounds. However, these formal approaches alone are not sufficient to solve the problem. In the present article, we describe reasons why discrimination with data can and typically does arise through the combined effects of human and machine-based reasoning, and argue that this requires a deeper understanding of the human side of decision-making with data mining. We describe results from a large-scale human-subjects experiment that investigated such decision-making, analyzing the reasoning that participants reported during their task to assess whether a loan request should or would be granted. We derive data protection by design strategies for making decision-making discrimination-aware in an accountable way, grounding these requirements in the accountability principle of the European Union General Data Protection Regulation, and outline how their implementations can integrate algorithmic, behavioral, and user interface factors.

Big DataVol. 5, No. 2 AnnouncementsCall for Papers: Special Issue on Computational Propaganda and Political Big DataPhilip N. Howard and Gillian BolsoverPhilip N. HowardSearch for more papers by this author and Gillian BolsoverSearch for more papers by this authorPublished Online:1 Jun 2017https://doi.org/10.1089/big.2017.29018.cfpAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Call for Papers: Special Issue on Computational Propaganda and Political Big Data." Big Data, 5(2), pp. 67–68FiguresReferencesRelatedDetails Volume 5Issue 2Jun 2017 InformationCopyright 2017, Mary Ann Liebert, Inc.To cite this article:Philip N. Howard and Gillian Bolsover.Call for Papers: Special Issue on Computational Propaganda and Political Big Data.Big Data.Jun 2017.67-68.http://doi.org/10.1089/big.2017.29018.cfpPublished in Volume: 5 Issue 2: June 1, 2017PDF download

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.