Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

Modeling and analysis on the propagation dynamics of modern email malware

Due to the critical security threats imposed by email-based malware in recent years, modeling the propagation dynamics of email malware becomes a fundamental technique for predicting its potential damages and developing effective countermeasures. Compared to earlier versions of email malware, modern email malware exhibits two new features, reinfection and self-start. Reinfection refers to the malware behavior that modern email malware sends out malware copies whenever any healthy or infected recipients open the malicious attachment. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. In the literature, several models are proposed for email malware propagation, but they did not take into account the above two features and cannot accurately model the propagation dynamics of modern email malware. To address this problem, we derive a novel difference equation based analytical model by introducing a new concept of virtual infected user. The proposed model can precisely present the repetitious spreading process caused by reinfection and self-start and effectively overcome the associated computational challenges. We perform comprehensive empirical and theoretical study to validate the proposed analytical model. The results show our model greatly outperforms previous models in terms of estimation accuracy. © 2013 IEEE.

Smartphone malware and its propagation modeling : A survey

Smartphones are pervasively used in society, and have been both the target and victim of malware writers. Motivated by the significant threat that presents to legitimate users, we survey the current smartphone malware status and their propagation models. The content of this paper is presented in two parts. In the first part, we review the short history of mobile malware evolution since 2004, and then list the classes of mobile malware and their infection vectors. At the end of the first part, we enumerate the possible damage caused by smartphone malware. In the second part, we focus on smartphone malware propagation modeling. In order to understand the propagation behavior of smartphone malware, we recall generic epidemic models as a foundation for further exploration. We then extensively survey the smartphone malware propagation models. At the end of this paper, we highlight issues of the current smartphone malware propagation models and discuss possible future trends based on our understanding of this topic. © © 2014 IEEE.

Building healthcare workers' confidence to work with same-sex parented families.

This article reports on a qualitative study of barriers and access to healthcare for same-sex attracted parents and their children. Focus groups were held with same-sex attracted parents to explore their experiences with healthcare providers and identify barriers and facilitators to access. Parents reported experiencing uncomfortable or anxiety-provoking encounters with healthcare workers who struggled to adopt inclusive or appropriate language to engage their family. Parents valued healthcare workers who were able to be open and honest and comfortably ask questions about their relationships and family. A separate set of focus groups were held with mainstream healthcare workers to identity their experiences and concerns about delivering equitable and quality care for same-sex parented families. Healthcare workers reported lacking confidence to actively engage with same-sex attracted parents and their children. This lack of confidence related to workers' unfamiliarity with same-sex parents, or lesbian, gay and bisexual culture, and limited opportunities to gain information or training in this area. Workers were seeking training and resources that offered information about appropriate language and terminology as well as concrete strategies for engaging with same-sex parented families. For instance, workers suggested they would find it useful to have a set of 'door opening' questions they could utilize to ask clients about their sexuality, relationship status or family make-up. This article outlines a set of guidelines for healthcare providers for working with same-sex parented families which was a key outcome of this study.

A proposed approach to systematically identify and monitor the corporate political activity of the food industry with respect to public health using publicly available information.

Unhealthy diets represent one of the major risk factors for non-communicable diseases. There is currently a risk that the political influence of the food industry results in public health policies that do not adequately balance public and commercial interests. This paper aims to develop a framework for categorizing the corporate political activity of the food industry with respect to public health and proposes an approach to systematically identify and monitor it. The proposed framework includes six strategies used by the food industry to influence public health policies and outcomes: information and messaging; financial incentive; constituency building; legal; policy substitution; opposition fragmentation and destabilization. The corporate political activity of the food industry could be identified and monitored through publicly available data sourced from the industry itself, governments, the media and other sources. Steps for country-level monitoring include identification of key food industry actors and related sources of information, followed by systematic data collection and analysis of relevant documents, using the proposed framework as a basis for classification of results. The proposed monitoring approach should be pilot tested in different countries as part of efforts to increase the transparency and accountability of the food industry. This approach has the potential to help redress any imbalance of interests and thereby contribute to the prevention and control of non-communicable diseases.

Economic Bias of Weather Forecasting: A Spatial Modeling Approach

The value of accurate weather forecast information is substantial. In this paper we examine competition among forecast providers and its implications for the quality of forecasts. A simple economic model shows that an economic bias geographical inequality in forecast accuracy arises due to the extent of the market. Using the unique data on daily high temperature forecasts for 704 U.S. cities, we find that forecast accuracy increases with population and income. Furthermore, the economic bias gets larger when the day of forecasting is closer to the target day; i.e. when people are more concerned about the quality of forecasts. The results hold even after we control for location-specific heterogeneity and difficulty of forecasting.

A sword with two edges: propagation studies on both positive and negative information in online social networks

Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people's behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people's preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

Building the capacity of family day care educators to promote children’s social and emotional wellbeing: Results of an exploratory cluster randomised-controlled trial

© 2015 Early Childhood Australia Inc. All rights reserved. THIS PAPER PRESENTS THE results of an exploratory cluster randomised-controlled trial that was used to pilot Thrive, a capacity-building program for family day care (FDC) educators. Participants were educators and coordinators from one FDC service in Melbourne, Australia. Data collection consisted of a survey including information on costs, an in-home quality of care observation and process evaluation. Data was collected over 12 months (2011–2012), at baseline and one, six and 12 months post-intervention. Positive caregiver interaction scores increased over time for the intervention group: F (3, 51.69) = 3.08, p < 0.05, and detached interaction scores decreased over time: F (3, 51.19) = 2.78, p < 0.05. Educators’ knowledge and confidence in children’s social and emotional wellbeing showed no significant change. Thrive gives important information about the challenges FDC educators face and is relevant to implementing changes in their education and support. For a program like Thrive to be successful in engaging educators, a stronger framework for supporting additional learning activities at both the FDC organisational and scheme level is warranted.

A predictive framework for modeling healthcare data with evolving clinical interventions

Medical interventions critically determine clinical outcomes. But prediction models either ignore interventions or dilute impact by building a single prediction rule by amalgamating interventions with other features. One rule across all interventions may not capture differential effects. Also, interventions change with time as innovations are made, requiring prediction models to evolve over time. To address these gaps, we propose a prediction framework that explicitly models interventions by extracting a set of latent intervention groups through a Hierarchical Dirichlet Process (HDP) mixture. Data are split in temporal windows and for each window, a separate distribution over the intervention groups is learnt. This ensures that the model evolves with changing interventions. The outcome is modeled as conditional, on both the latent grouping and the patients' condition, through a Bayesian logistic regression. Learning distributions for each time-window result in an over-complex model when interventions do not change in every time-window. We show that by replacing HDP with a dynamic HDP prior, a more compact set of distributions can be learnt. Experiments performed on two hospital datasets demonstrate the superiority of our framework over many existing clinical and traditional prediction frameworks.

Speed up health research through topic modeling of coded clinical data

Although random control trial is the gold standard in medical research, researchers are increasingly looking to alternative data sources for hypothesis generation and early-stage evidence collection. Coded clinical data are collected routinely in most hospitals. While they contain rich information directly related to the real clinical setting, they are both noisy and semantically diverse, making them difficult to analyze with conventional statistical tools. This paper presents a novel application of Bayesian nonparametric modeling to uncover latent information in coded clinical data. For a patient cohort, a Bayesian nonparametric model is used to reveal the common comorbidity groups shared by the patients and the proportion that each comorbidity group is reflected individual patient. To demonstrate the method, we present a case study based on hospitalization coding from an Australian hospital. The model recovered 15 comorbidity groups among 1012 patients hospitalized during a month. When patients from two areas of unequal socio-economic status were compared, it reveals higher prevalence of diverticular disease in the region of lower socio-economic status. The study builds a convincing case for routine coded data to speed up hypothesis generation.

HorusCML: context-aware domain-specific visual languages designer

The objective behind building domain-specific visual languages (DSVLs) is to provide users with the most appropriate concepts and notations that best fit with their domain and experience. However, the existing DSVL designers do not support integrating environment and user context information when modeling, editing or viewing DSVL models at different locations, permissions, devices, etc. In this paper, we introduce HorusCML, a context-aware DSVL designer, which supports DSVL experts in integrating necessary context details within their DSVLs. The resultant DSVLs can reflect different facets, layouts, and behaviours according to context it is used in. We show a case study on developing a context-aware data flow diagram DSVL tool using HorusCML.

Modeling malicious activities in cyber space

Cyber attacks are an unfortunate part of society as an increasing amount of critical infrastructure is managed and controlled via the Internet. In order to protect legitimate users, it is critical for us to obtain an accurate and timely understanding of our cyber opponents. However, at the moment we lack effective tools to do this. In this article we summarize the work on modeling malicious activities from various perspectives, discuss the pros and cons of current models, and present promising directions for possible efforts in the near future.

Consensus building in a local context for the AHP-GDM with the individual numerical scale and prioritization method

The methodology for selecting the individual numerical scale and prioritization method has recently been presented and justified in the analytic hierarchy process (AHP). In this study, we further propose a novel AHP-group decision making (GDM) model in a local context (a unique criterion), based on the individual selection of the numerical scale and prioritization method. The resolution framework of the AHP-GDM with the individual numerical scale and prioritization method is first proposed. Then, based on linguistic Euclidean distance (LED) and linguistic minimum violations (LMV), the novel consensus measure is defined so that the consensus degree among decision makers who use different numerical scales and prioritization methods can be analyzed. Next, a consensus reaching model is proposed to help decision makers improve the consensus degree. In this consensus reaching model, the LED-based and LMV-based consensus rules are proposed and used. Finally, a new individual consistency index and its properties are proposed for the use of the individual numerical scale and prioritization method in the AHP-GDM. Simulation experiments and numerical examples are presented to demonstrate the validity of the proposed model.

Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.

Systematic examination of publicly-available information reveals the diverse and extensive corporate political activity of the food industry in Australia

BACKGROUND: The political influence of the food industry, referred to as corporate political activity (CPA), represents a potential barrier to the development and implementation of effective public health policies for non-communicable diseases prevention. This paper reports on the feasibility and limitations of using publicly-available information to identify and monitor the CPA of the food industry in Australia. METHODS: A systematic search was conducted for information from food industry, government and other publicly-available data sources in Australia. Data was collected in relation to five key food industry actors: the Australian Food and Grocery Council; Coca Cola; McDonald's; Nestle; and Woolworths, for the period January 2012 to February 2015. Data analysis was guided by an existing framework for classifying CPA strategies of the food industry. RESULTS: The selected food industry actors used multiple CPA strategies, with 'information and messaging' and 'constituency building' strategies most prominent. CONCLUSIONS: The systematic analysis of publicly-available information over a limited period was able to identify diverse and extensive CPA strategies of the food industry in Australia. This approach can contribute to accountability mechanisms for NCD prevention.