Surveillance of communicable diseases using social media: A systematic review.

BACKGROUND: Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media. OBJECTIVE: To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases. METHODOLOGY: Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation. CONCLUSION: Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.

Using Machine Learning for Pharmacovigilance: A Systematic Review.

Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance.

The role of time in the relation between perceived job insecurity and perceived job performance.

BACKGROUND: Insufficient evidence exists that can explain two conflicting views (i.e. positive and negative relationship) regarding the effect of job insecurity on job performance. OBJECTIVE: To investigate the importance of time in explaining these ambiguous views. A positive association was expected cross-sectionally and a negative relationship longitudinally. I hypothesized that available coping resources may delay the negative effect on job performance until being exhausted. METHODS: Longitudinal self-reported data of 928 participants were used. Job performance was operationalized as core task performance and productivity loss. Cross-sectional and longitudinal associations were analyzed using linear and logistic regressions. Duration analyses were performed using the two-year duration of job insecurity. RESULTS: Short-term and long-term, job insecurity was only related with increased productivity loss. No evidence was found for core task performance. The duration of job insecurity, and chronic job insecurity in particular, did not predict core task performance or productivity loss two years later. CONCLUSIONS: The factor time, operationalized as the time of follow-up and the duration of exposure to job insecurity, did not clarify the conflicting views. Managers should be more aware of the adverse effects of using job insecurity as a motivational strategy to increase job performance.