Identification of Consumer Adverse Drug Reaction Messages on Social Media

The prevalence of social media has resulted in spikes of data on the Internet which can have potential use to assist in many aspects of human life. One prospective use of the data is in the development of an early warning system to monitor consumer Adverse Drug Reactions (ADRs). The direct reporting of ADRs by consumers is playing an increasingly important role in the world of pharmacovigilance. Social media provides patients a platform to exchange their experiences regarding the use of certain drugs. However, the messages posted on those social media networks contain both ADR related messages (positive examples) and non-ADR related messages (negative examples). In this paper, we integrate text mining and partially supervised learning methods to automatically extract and classify messages posted on social media networks into positive and negative examples. Our findings can provide managerial insights into how social media analytics can improve not only postmarketing surveillance, but also other problem domains where large quantity of user-generated content is available

Front Pharmacol

Drug misuse may happen when patients do not follow the prescriptions and do actions which lead to potentially harmful situations, such as intakes of incorrect dosage (overuse or underuse) or drug use for indications different from those prescribed. Although such situations are dangerous, patients usually do not report the misuse of drugs to their physicians. Hence, other sources of information are necessary for studying these issues. We assume that online health fora can provide such information and propose to exploit them. The general purpose of our work is the automatic detection and classification of drug misuses by analysing user-generated data in French social media. To this end, we propose a multi-step method, the main steps of which are: (1) indexing of messages with extended vocabulary adapted to social media writing; (2) creation of typology of drug misuses; and (3) automatic classification of messages according to whether they contain drug misuses or not. We present the results obtained at different steps and discuss them. The proposed method permit to detect the misuses with up to 0.773 F-measure

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system

Reports of substandard medicines: a lexicographic analysis of the Brazilian Health Surveillance Report System

Regulatory agencies are responsible for collecting and evaluating spontaneous reports of suspected problems related to medications, including those from substandard medicines (SM). Objectives: The aim was to evaluate the profile of SM reports submitted to the Brazilian Health Surveillance Notification System (Notivisa) and classify these reports objectively by means of lexicographic analysis. Methods: Was extracted all SM reports available in Notivisa during the period 1 January 2007 to 31 December 2017. Descriptive statistics were performed and the reasons for SM reporting were standardized (using OpenRefine and Microsoft Excel). The following analyses were performed using IRAMuTeQ 0.7 alpha2: lexicographic analysis to obtain the frequency of active words; descending hierarchical classification (DHC) to categorize the active words into lexical classes; factorial correspondence analysis (FCA) to obtain graphs of the classes. Approved by the Ethics Committee of the Hospital do Trabalhador/SES/PR CAAE 81873417.3.0000.5225 (protocol number: 2.506.594). Results: A total of 61,775 reports were analyzed, most of them reported by hospitals (46%). The DHC of the reasons for SM produced four classes visualized in the FCA: (i) packaging problems (16%) mainly leakages/opening issues; (ii) inadequate drug identification (22%), such as illegible label information; (iii) stability and contamination issues (11%) such as presence of particles; (iv) damaged tablets/blisters (23%) mainly broken tablets. Most SM (52%) were solutions for parenteral use; sodium chloride (9%), glucose and dipyrone (3%) were the products with most complaints. Conclusions: The reasons for SM reporting can be objectively classified into classes that represent the main problems submitted to Notivisa. This classification could guide the standardization of SM reporting and contribute to improving surveillance reporting systems worldwide

Big data and diabetes: the applications of big data for diabetes care now and in the future

Aims: Review the current applications of Big Data in diabetes care and consider the future potential. Methods: Scoping study of the academic literature on Big Data and diabetes care. Results: Healthcare data are being produced at ever-increasing rates, and this information has the potential to transform the provision of diabetes care. Big Data is beginning to have an impact on diabetes care through data research. The use of Big Data for routine clinical care is still a future application. Conclusions: Vast amounts of healthcare data are already being produced, and the key is harnessing these to produce actionable insights. Considerable development work is required to achieve these goals

Mining Social Media for Perceptions and Trends on HIV Pre-Exposure Prophylaxis

Pre-Exposure Prophylaxis (PrEP) is an approach for preventing the human immunodeficiency virus (HIV), which entails the administration of antiretroviral medication to high-risk seronegative persons. If taken correctly, PrEP can reduce HIV infection risk by more than 90%. The aim of this study was to identify and examine PrEP-related perceptions and trends discussed on Twitter. Using open-source technologies, text-mining and interactive visualisation techniques, a comprehensive data gathering and analytics Web-based platform was developed to facilitate the study objectives. Our results demonstrate that monitoring of PrEP-related discussions on Twitter can be detected over time and valuable insights can be obtained concerning issues of PrEP awareness, expressed opinions, perceived barriers and key discussion points on its adoption. The proposed platform could support public-health professionals and policy makers in PrEP monitoring, facilitating informed decision making and strategy planning for efficient HIV combination prevention

Scaling up health knowledge at European level requires sharing integrated data: An approach for collection of database specification

Computerized health care databases have been widely described as an excellent opportunity for research. The availability of “big data” has brought about a wave of innovation in projects when conducting health services research. Most of the available secondary data sources are restricted to the geographical scope of a given country and present heterogeneous structure and content. Under the umbrella of the European Innovation Partnership on Active and Healthy Ageing, collaborative work conducted by the partners of the group on “adherence to prescription and medical plans” identified the use of observational and large-population databases to monitor medication-taking behavior in the elderly. This article describes the methodology used to gather the information from available databases among the Adherence Action Group partners with the aim of improving data sharing on a European level. A total of six databases belonging to three different European countries (Spain, Republic of Ireland, and Italy) were included in the analysis. Preliminary results suggest that there are some similarities. However, these results should be applied in different contexts and European countries, supporting the idea that large European studies should be designed in order to get the most of already available databases

Herb-Drug Interactions: A Holistic Decision Support System in Healthcare

Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events