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User engagement (UE) is the quality of user experience that emphasizes any positive aspects of interaction with an application, and particularly the phenomena associated with being captivated by certain features included in it, thus being motivated to use it. In the context of mobile applications, measuring UE could provide insights to further explain usage behaviors, allowing developers, and product managers, to gain a better understanding of how users utilize their applications, and what drives their engagement with them. Numerous methods have been proposed in literature to measure UE in domains such as online services; however, not much had been done to model UE in the context of mobile applications. In response to this problem, our study proposes to measure UE with mobile applications by analyzing temporal changes in a defined set of usage metrics, yielding a general metric, a mobile application user engagement (MAUE) score, which is a linear combination of the UE time series metrics, accounting for the largest amount of the variance in usage data, and extracted by principal component analysis (PCA). Our proposed approach has been applied to the behavioral data of 40,004 unique users of The Weather Company mobile application. Our results indicate that time-dependent fluctuations of the MAUE score are characterized with a power-law decrease, in accordance with the power law of practice, suggesting different levels of UE stability for the different mobile platforms (i.e., IOS, Android). Additionally, the Multidimensional scaling distance between clusters of variables loadings, and among the variables loadings within each cluster with regards to the amount of usage days, could be used to map the UE motivations and thus provide product managers an improved understanding and prediction ability of the influence of different app updates and product interventions on UE.

A Cubist model for hospital Length of Stay (LOS) is proposed.The groups of cases covered by the Cubist rules differ in their characteristics.The LOS primarily depends on historical variables such as number of admissions.Applying CARMA algorithm allows discovery of important relations among variables.A method to separate the cases by their level of Cubist error. A model that accurately predicts, at the time of admission, the Length of Stay (LOS) for hospitalized patients could be an effective tool for healthcare providers. It could enable early interventions to prevent complications, enabling more efficient utilization of manpower and facilities in hospitals. In this study, we apply a regression tree (Cubist) model for predicting the LOS, based on static inputs, that is, values that are known at the time of admission and that do not change during patient's hospital stay. The model was trained and validated on de-identified administrative data from the Veterans Health Administration (VHA) hospitals in Pittsburgh, PA. We chose to use a Cubist model because it produced more accurate predictions than did alternative techniques. In addition, tree models enable us to examine the classification rules learned from the data, in order to better understand the factors that are most correlated with hospital LOS. Cubist recursively partitions the data set as it estimates linear regressions for each partition, and the error level differs for different partitions, so that it is possible to deduce what are the characteristics of patients whose LOS can be accurately predicted at admission, and what are the characteristics of patients for whom the LOS estimate at that point in time is more highly uncertain. For example, our model indicates that the prediction error is greater for patients who had more admissions in the recent past, and for those who had longer previous hospital stays. Our approach suggests that mapping the cases into a higher dimensional space, using a Radial Basis Function (RBF) kernel, helps to separate them by their level of Cubist error, using a Support Vector Machine (SVM).

The hospital length-of-stay (LOS), and the time between a discharge and the next admission, are important measures of healthcare utilization, and are generally positively skewed. We model the state transitions of CHF patients, using data from the Veterans Health Administration (VHA), by fitting a Coxian phase-type distribution to their LOS data, and extract the associated states in the latent Markov process. Selecting an appropriate number of phases helps to account for some heterogeneity among different LOS groups within the hospital, and provides a way to interpret each added covariate. By analyzing the strength of the connections among patient social, clinical, and historical characteristics within each group, the associated readmission risk may be estimated. For example, we found that groups with a greater LOS tended to have a greater proportion of patients from nursing home care. Nursing home care patients, who belong to the greater LOS group, tended to have a decreased readmission risk. Thus, by inc...

Recent developments in the field of fluorescence lifetime imaging microscopy (FLIM) techniques allow the use of high repetition rate light sources in live cell experiments. For light sources with a repetition rate of 20-100 MHz, the time-correlated single photon counting (TCSPC) FLIM systems suffer serious dead time related distortions, known as "inter-pulse pile-up". The objective of this paper is to present a new method to quantify the level of signal distortion in TCSPC FLIM experiments, in order to determine the most efficient laser repetition rate for different FLT ranges. Optimization of the F -value, which is the relation between the relative standard deviation (RSD) in the measured FLT to the RSD in the measured fluorescence intensity (FI), allows quantification of the level of FI signal distortion, as well as determination of the correct FLT of the measurement. It is shown that by using a very high repetition rate (80 MHz) for samples characterized by high real FLT's (4-5 ns), virtual short FLT components are added to the FLT histogram while a F -value that is higher than 1 is obtained. For samples characterized with short real FLT's, virtual long FLT components are added to the FLT histogram with the lower repetition rate (20-50 MHz), while by using a higher repetition rate (80 MHz) the "inter-pulse pile-up" is eliminated as the F -value is close to 1.

A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers1, Lisa Simpson2 1Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, 20850, USA; 2AcademyHealth, Washington, DC, 20036, USA For the second year in a row, we are pleased to be able to share the proceedings of the Annual Conference on the Science of Dissemination and Implementation in Health, a large meeting reflecting the expanding and evolving research field that seeks to optimize the use of evidence, interventions, and tools from health research within the myriad of settings where people receive health care, make health-related decisions, and increase knowledge of influences on the health of the population. We once again benefitted from a strong partnership, co-led by AcademyHealth and the National Institutes of Health (NIH), with co-sponsorship from the Agency for Healthcare Research and Quality (AHRQ), the Patient Centered Outcomes Research Institute (PCORI), the Robert Wood Johnson Foundation (RWJF), the US Department of Veterans Affairs (VA), and the WT Grant Foundation. In addition, we benefitted from the collaboration of staff from the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). NIH and AcademyHealth again co-led the program planning committee, which focused on the development of the plenary sessions, and convened a scientific advisory panel to suggest speakers and advise on the overall conference development. The planning committee identified four key areas around which to focus the plenary panels and keynote address. Dr. America Bracho, M.D., M.P.H., Executive Director of Latino Health Access in Orange County, California, spoke about the opportunities for implementation science to inform efforts to improve community health and engage underserved populations. The three plenary panels each focused on a significant future direction for dissemination and implementation (D & I) research: the interface between D&I science and population health, emerging opportunities for global implementation science, and the challenges around implementation of precision medicine. The plenary sessions were complemented by facilitated lunchtime discussions on the same three topics, which offered participants an opportunity to identify key research questions for each and brainstorm next steps. Synopses of the lunchtime discussions are included in this supplement. Given the overwhelming success of the 2014 conference and the large number of abstracts received in 2014 (660), the program planning committee identified eight program tracks for abstract submitters to respond to, and through which the concurrent sessions of the conference would be organized. These tracks—Behavioral Health, Big Data and Technology for Dissemination and Implementation Research, Clinical Care Settings, Global Dissemination and Implementation, Promoting Health Equity and Eliminating Disparities, Health Policy Dissemination and Implementation, Prevention and Public Health, and Models, Measures and Methods— were designed to enable conference participants to follow a consistent theme across the multiple sessions of the conference and form the structure of this supplement. The call for abstracts, including individual paper presentations, individual posters and panel presentations, resulted in 515 submissions, spread across the eight thematic tracks. Over one hundred reviewers devoted their time to ensuring a comprehensive and expert review, and reviews were conducted within each track and coordinated by the track leads. For the final program, 64 oral presentations, 12 panels, and 263 posters were presented over the two-day meeting. Slides for the oral presentations and panels (with the agreement of the authors) were posted on the conference website (http://diconference.academyhealth.org/archives/2015archives) and all abstracts were included on the conference webapp (https://academyhealth.confex.com/academyhealth/2015di/meetingapp.cgi). This supplement has compiled the abstracts for presented papers, panel sessions, and lunchtime discussions from the 8th Annual Meeting on the Science of Dissemination and Implementation in Health: Optimizing Personal and Population Health. We are pleased to have the abstracts from the conference together in one volume once again, and look forward to the 9th Annual meeting, scheduled for December in Washington, D.C.

A mixed-ensemble model for hospital readmission is proposed.The mixed-ensemble model enables controlling the tradeoff between reasoning transparency and predictive accuracy.The mixed-ensemble model increases the classification accuracy for positive readmission instances.An optimization approach for the mixed-ensemble model is proposed.The mixed-ensemble model has been implemented for predicting all-cause hospital readmissions of CHF patients. ObjectiveA hospital readmission is defined as an admission to a hospital within a certain time frame, typically thirty days, following a previous discharge, either to the same or to a different hospital. Because most patients are not readmitted, the readmission classification problem is highly imbalanced. Materials and methodsWe developed a hospital readmission predictive model, which enables controlling the tradeoff between reasoning transparency and predictive accuracy, by taking into account the unique characteristics of the learned database. A boosted C5.0 tree, as the base classifier, was ensembled with a support vector machine (SVM), as a secondary classifier. The models were induced and validated using anonymized administrative records of 20,321 inpatient admissions, of 4840 Congestive Heart Failure (CHF) patients, at the Veterans Health Administration (VHA) hospitals in Pittsburgh, from fiscal years (FY) 2006 through 2014. ResultsThe SVM predictions are characterized by greater sensitivity values (true positive rates) than are the C5.0 predictions, for a wider range of cut off values of the ROC curve, depending on a predefined confidence threshold for the base C5.0 classifier. The total accuracy for the ensemble ranges from 81% to 85%. Different predictors, including comorbidities, lab values, and vitals, play different roles in the two models. ConclusionsThe mixed-ensemble model enables easy and fast exploratory knowledge discovery of the database, and a control of the classification error for positive readmission instances. Implementation of this ensembling method for predicting all-cause hospital readmissions of CHF patients allows overcoming some of the limitations of the classifiers considered individually, and of other traditional ensembling methods. It also increases the classification accuracy for positive readmission instances, particularly when strong predictors are not available.

A general framework to include fluctuations in the single molecule fluorescence intensity (FI) signal due to random changes in molecule dipole orientation was introduced at Optics Express (21, 2007). By assuming continuous changes in dipole orientation described by Brownian rotational diffusion, this research derives the probability density function (PDF) equation of FI fluctuations. Solution of the proposed equation for several limiting cases and different correlation times yields the short time behavior of FI fluctuations. Monte Carlo simulations results, in accordance with those found in theory will be presented during our talk.