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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)