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In this expository paper we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and public health surveillance. We encourage researchers in the industrial process monitoring area to work on developing and comparing the performance of social network monitoring methods. We also discuss some of the issues in social network monitoring and give a number of research ideas.

In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target. We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios.

BackgroundTelemonitoring is becoming increasingly important for the management of patients with chronic conditions, especially in countries with large distances such as Australia. However, despite large national investments in health information technology, little policy work has been undertaken in Australia in deploying telehealth in the home as a solution to the increasing demands and costs of managing chronic disease.ObjectiveThe objective of this trial was to evaluate the impact of introducing at-home telemonitoring to patients living with chronic conditions on health care expenditure, number of admissions to hospital, and length of stay (LOS).MethodsA before and after control intervention analysis model was adopted whereby at each location patients were selected from a list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. Test patients were supplied with a telehealth vital signs monitor and were remotely managed by a trained clinical care coordinator, while control patients continued to receive usual care. A total of 100 test patients and 137 control patients were analyzed. Primary health care benefits provided to Australian patients were investigated for the trial cohort. Time series data were analyzed using linear regression and analysis of covariance for a period of 3 years before the intervention and 1 year after.ResultsThere were no significant differences between test and control patients at baseline. Test patients were monitored for an average of 276 days with 75% of patients monitored for more than 6 months. Test patients 1 year after the start of their intervention showed a 46.3% reduction in rate of predicted medical expenditure, a 25.5% reduction in the rate of predicted pharmaceutical expenditure, a 53.2% reduction in the rate of predicted unscheduled admission to hospital, a 67.9% reduction in the predicted rate of LOS when admitted to hospital, and a reduction in mortality of between 41.3% and 44.5% relative to control patients. Control patients did not demonstrate any significant change in their predicted trajectory for any of the above variables.ConclusionsAt-home telemonitoring of chronically ill patients showed a statistically robust positive impact increasing over time on health care expenditure, number of admissions to hospital, and LOS as well as a reduction in mortality.Trial RegistrationRetrospectively registered with the Australian and New Zealand Clinical Trial Registry ACTRN12613000635763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030 (Archived by WebCite at http://www.webcitation.org/6sxqjkJHW)

In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

Daily counts of computer records of hospital emergency department arrivals grouped according to diagnosis (called here syndrome groupings) can be monitored by epidemiologists for changes in frequency that could provide early warning of bioterrorism events or naturally occurring disease outbreaks and epidemics. This type of public health surveillance is sometimes called syndromic surveillance. We used transitional Poisson regression models to obtain one-day-ahead arrival forecasts. Regression parameter estimates and forecasts were updated for each day using the latest 365 days of data. The resulting time series of recursive estimates of parameters such as the amplitude and location of the seasonal peaks as well as the one-day-ahead forecasts and forecast errors can be monitored to understand changes in epidemiology of each syndrome grouping.The counts for each syndrome grouping were autocorrelated and non-homogeneous Poisson. As such, the main methodological contribution of the article is the adaptation of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) plans for monitoring non-homogeneous counts. These plans were valid for small counts where the assumption of normally distributed one-day-ahead forecasts errors, typically used in other papers, breaks down. In addition, these adaptive plans have the advantage that control limits do not have to be trained for different syndrome groupings or aggregations of emergency departments.Conventional methods for signaling increases in syndrome grouping counts, Shewhart, CUSUM, and EWMA control charts of the standardized forecast errors were also examined. Shewhart charts were, at times, insensitive to shifts of interest. CUSUM and EWMA charts were only reasonable for large counts. We illustrate our methods with respiratory, influenza, diarrhea, and abdominal pain syndrome groupings.

BackgroundTelehealth services based on at-home monitoring of vital signs and the administration of clinical questionnaires are being increasingly used to manage chronic disease in the community, but few statistically robust studies are available in Australia to evaluate a wide range of health and socio-economic outcomes. The objectives of this study are to use robust statistical methods to research the impact of at home telemonitoring on health care outcomes, acceptability of telemonitoring to patients, carers and clinicians and to identify workplace cultural factors and capacity for organisational change management that will impact on large scale national deployment of telehealth services. Additionally, to develop advanced modelling and data analytics tools to risk stratify patients on a daily basis to automatically identify exacerbations of their chronic conditions.Methods/DesignA clinical trial is proposed at five locations in five states and territories along the Eastern Seaboard of Australia. Each site will have 25 Test patients and 50 case matched control patients. All participants will be selected based on clinical criteria of at least two hospitalisations in the previous year or four or more admissions over the last five years for a range of one or more chronic conditions. Control patients are matched according to age, sex, major diagnosis and their Socio-Economic Indexes for Areas (SEIFA). The Trial Design is an Intervention control study based on the Before-After-Control-Impact (BACI) design.DiscussionOur preliminary data indicates that most outcome variables before and after the intervention are not stationary, and accordingly we model this behaviour using linear mixed-effects (lme) models which can flexibly model within-group correlation often present in longitudinal data with repeated measures. We expect reduced incidence of unscheduled hospitalisation as well as improvement in the management of chronically ill patients, leading to better and more cost effective care. Advanced data analytics together with clinical decision support will allow telehealth to be deployed in very large numbers nationally without placing an excessive workload on the monitoring facility or the patient's own clinicians.Trial registrationRegistered with Australian New Zealand Clinical Trial Registry on 1st April 2013. Trial ID: ACTRN12613000635763