Sentiment analysis of health care tweets: review of the methods used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first

Systematic review of hospital readmissions in stroke patients

Background Previous evidence on factors and causes of readmissions associated with high-impact users of stroke is scanty. The aim of the study was to investigate common causes and pattern of short- and long-term readmissions stroke patients by conducting a systematic review of studies using hospital administrative data. Common risk factors associated with the change of readmission rate were also examined. Methods The literature search was conducted from 15th February to 15th March 2016 using various databases, such as Medline, Embase, and Web of Science. Results There were total of 24 studies (n=2,126,617) included in the review. Only 4 studies assessed causes of readmissions in stroke patients with the follow-up duration from 30 days to 5 years. Common causes of readmissions in majority of the studies were recurrent stroke, infections and cardiac conditions. Common patient-related risk factors associated with increased readmission rate were age and history of coronary heart disease, heart failure, renal disease, respiratory disease, peripheral arterial disease and diabetes. Among stroke-related factors, length of stay of index stroke admission was associated with increased readmission rate, followed by bowel incontinence, feeding tube and urinary catheter. Conclusion Although risk factors and common causes of readmission were identified, but none of the previous studies investigated causes and their sequence of readmissions among high-impact stroke users

Community detection and role identification in directed networks: understanding the Twitter network of the care.data debate

With the rise of social media as an important channel for the debate and discussion of public affairs, online social networks such as Twitter have become important platforms for public information and engagement by policy makers. To communicate effectively through Twitter, policy makers need to understand how influence and interest propagate within its network of users. In this chapter we use graph-theoretic methods to analyse the Twitter debate surrounding NHS Englands controversial care.data scheme. Directionality is a crucial feature of the Twitter social graph - information flows from the followed to the followers - but is often ignored in social network analyses; our methods are based on the behaviour of dynamic processes on the network and can be applied naturally to directed networks. We uncover robust communities of users and show that these communities reflect how information flows through the Twitter network. We are also able to classify users by their differing roles in directing the flow of information through the network. Our methods and results will be useful to policy makers who would like to use Twitter effectively as a communication medium

Review on some Stefan Problems for Particle Dissolution in Solid Metallic Alloys

This paper is a review of a suite of mathematical models of increasing complexity on particle dissolution in metallic alloys. This work deals with models for multi-component particle dissolution in multi-component alloys, where various chemical species diffuse simultaneously, and a two-dimensional model incorporating interfacial reactions as in the model of Nolfi [1]. The work is mathematically rigorous where asymptotic solutions and solution bounds are derived but is also of a practical nature as particle dissolution kinetics is modelled for industrially relevant conditions

Assessing the potential outcomes of achieving the World Health Organization global non-communicable diseases targets for risk factors by 2025: is there also an economic dividend?

International audienceOBJECTIVES:This study assesses the change in premature mortality and in morbidity under the scenario of meeting the World Health Organization (WHO) global targets for non-communicable disease (NCD) risk factors (RFs) by 2025 in France. It also estimates medical expenditure savings because of the reduction of NCD burden.STUDY DESIGN:A microsimulation model is used to predict the future health and economic outcomes in France.METHODS:A 'RF targets' scenario, assuming the achievement of the six targets on RFs by 2025, is compared to a counterfactual scenario with respect to disability-adjusted life years and healthcare costs differences.RESULTS:The achievement of the RFs targets by 2025 would save about 25,300 (and 75,500) life years in good health in the population aged 25-64 (respectively 65+) years on average every year and would help to reduce healthcare costs by about €660 million on average per year, which represents 0.35% of the current annual healthcare spending in France. Such a reduction in RFs (net of the natural decreasing trend in mortality) would contribute to achieving about half of the 2030 NCD premature mortality target in France.CONCLUSIONS:The achievement of the RF targets would lead France to save life years and life years in good health in both working-age and retired people and would modestly reduce healthcare expenditures. To achieve RFs targets and to curb the growing burden of NCDs, France has to strengthen existing and implement new policy interventions

Promoting sport and physical activity in Italy: A costeffectiveness analysis of seven innovative public health policies

Background. Inactive lifestyles are a key risk factor underpinning the development of many chronic diseases, yet more than half of the Italian population does not meet WHO thresholds for at least moderate physical activity. This study aims to make the economic case to upscale investments in policy actions to promote exercise and physical activity. Study design. Modelling-based cost-effectiveness analysis in Italy Methods. The study assesses the impact on health and healthcare expenditure of seven public health policies to promote exercise and physical activity against a business as usual scenario. Assessed policies include: promotion of active transport, workplace sedentarily interventions, investments in sports and recreation, mass media campaigns, prescription of physical activity in primary care, school-based interventions and mobile apps. Results. Public policies to promote exercise have the potential to improve population health and produce savings in healthcare expenditure. Assessed policies can avoid hundreds of cases of cardiovascular diseases and diabetes per year and tens of cases of cancer resulting in gains in DALYs in the order of thousands per year. In the medium-term, the vast majority of policies show excellent cost-effectiveness ratio, below internationally recognized thresholds. Conclusions. Investing in policies to promote active lifestyles is a good investment for Italy

The impact of diabetes on multiple avoidable admissions: a cross-sectional study

Background Multiple admissions for ambulatory care sensitive conditions (ACSC) are responsible for an important proportion of health care expenditures. Diabetes is one of the conditions consensually classified as an ACSC being considered a major public health concern. The aim of this study was to analyse the impact of diabetes on the occurrence of multiple admissions for ACSC. Methods We analysed inpatient data of all public Portuguese NHS hospitals from 2013 to 2015 on multiple admissions for ACSC among adults aged 18 or older. Multiple ACSC users were identified if they had two or more admissions for any ACSC during the period of analysis. Two logistic regression models were computed. A baseline model where a logistic regression was performed to assess the association between multiple admissions and the presence of diabetes, adjusting for age and sex. A full model to test if diabetes had no constant association with multiple admissions by any ACSC across age groups. Results Among 301,334 ACSC admissions, 144,209 (47.9%) were classified as multiple admissions and from those, 59,436 had diabetes diagnosis, which corresponded to 23,692 patients. Patients with diabetes were 1.49 times (p < 0,001) more likely to be admitted multiple times for any ACSC than patients without diabetes. Younger adults with diabetes (18–39 years old) were more likely to become multiple users. Conclusion Diabetes increases the risk of multiple admissions for ACSC, especially in younger adults. Diabetes presence is associated with a higher resource utilization, which highlights the need for the implementation of adequate management of chronic diseases policies.NOVASaudeinfo:eu-repo/semantics/publishedVersio

One-Sided Position-Dependent Smoothness-Increasing Accuracy-Conserving (SIAC) Filtering Over Uniform and Non-Uniform Meshes

In this paper, we introduce a new position-dependent Smoothness-Increasing Accuracy-Conserving (SIAC) filter that retains the benefits of position dependence while ameliorating some of its shortcomings. As in the previous position-dependent filter, our new filter can be applied near domain boundaries, near a discontinuity in the solution, or at the interface of different mesh sizes; and as before, in general, it numerically enhances the accuracy and increases the smoothness of approximations obtained using the discontinuous Galerkin (dG) method. However, the previously proposed position-dependent one-sided filter had two significant disadvantages: (1) increased computational cost (in terms of function evaluations), brought about by the use of $4k+1$ central B-splines near a boundary (leading to increased kernel support) and (2) increased numerical conditioning issues that necessitated the use of quadruple precision for polynomial degrees of $k\ge 3$ for the reported accuracy benefits to be realizable numerically. Our new filter addresses both of these issues --- maintaining the same support size and with similar function evaluation characteristicsas the symmetric filter in a way that has better numerical conditioning --- making it, unlike its predecessor, amenable for GPU computing. Our new filter was conceived by revisiting the original error analysis for superconvergence of SIAC filters and by examining the role of the B-splines and their weights in the SIAC filtering kernel. We demonstrate, in the uniform mesh case, that our new filter is globally superconvergent for $k=1$ and superconvergent in the interior (e.g., region excluding the boundary) for $k\ge2$. Furthermore, we present the first theoretical proof of superconvergence for postprocessing over smoothly varying meshes, and explain the accuracy-order conserving nature of this new filter when applied to certain non-uniform meshes cases. We provide numerical examples supporting our theoretical results and demonstrating that our new filter, in general, enhances the smoothness and accuracy of the solution. Numerical results are presented for solutions of both linear and nonlinear equation solved on both uniform and non-uniform one- and two-dimensional meshes