Identifying Potential Adverse Effects Using the Web: A New Approach to Medical Hypothesis Generation

Medical message boards are online resources where users with a particular condition exchange information, some of which they might not otherwise share with medical providers. Many of these boards contain a large number of posts and contain patient opinions and experiences that would be potentially useful to clinicians and researchers. We present an approach that is able to collect a corpus of medical message board posts, de-identify the corpus, and extract information on potential adverse drug effects discussed by users. Using a corpus of posts to breast cancer message boards, we identified drug event pairs using co-occurrence statistics. We then compared the identified drug event pairs with adverse effects listed on the package labels of tamoxifen, anastrozole, exemestane, and letrozole. Of the pairs identified by our system, 75–80% were documented on the drug labels. Some of the undocumented pairs may represent previously unidentified adverse drug effects

The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions

The management of drug-drug interactions (DDIs) is a critical issue resulting from the overwhelming amount of information available on them. Natural Language Processing (NLP) techniques can provide an interesting way to reduce the time spent by healthcare professionals on reviewing biomedical literature. However, NLP techniques rely mostly on the availability of the annotated corpora. While there are several annotated corpora with biological entities and their relationships, there is a lack of corpora annotated with pharmacological substances and DDIs. Moreover, other works in this field have focused in pharmacokinetic (PK) DDIs only, but not in pharmacodynamic (PD) DDIs. To address this problem, we have created a manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts. This fined-grained corpus has been annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions. The quality and consistency of the annotation process has been ensured through the creation of annotation guidelines and has been evaluated by the measurement of the inter-annotator agreement between two annotators. The agreement was almost perfect (Kappa up to 0.96 and generally over 0.80), except for the DDIs in the MedLine database (0.55-0.72). The DDI corpus has been used in the SemEvaI 2013 DDIExtraction challenge as a gold standard for the evaluation of information extraction techniques applied to the recognition of pharmacological substances and the detection of DDIs from biomedical texts. DDIExtraction 2013 has attracted wide attention with a total of 14 teams from 7 different countries. For the task of recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was F1 of 65.1%.Funding: This work was supported by the EU project TrendMiner [FP7-ICT287863], by the project MULTIMEDICA [TIN2010- 20644-C03-01], and by the Research Network MA2VICMR [S2009/TIC-1542].Publicad

Extraction and Classification of Drug-Drug Interaction from Biomedical Text Using a Two-Stage Classifier

One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, mechanism, and int. To find the set of best features for classification, we explored many features, including stemmed words, bigrams, part of speech tags, verb lists, parse tree information, mutual information, and similarity measures, among others. As the system faced two different classification tasks, binary and multi-class, we also explored various classifiers in each stage. Our results show that the best performing classifier in both stages was Support Vector Machines, and the best performing features were 1000 top informative words and part of speech tags between two main drugs. We obtained an F-Measure of 0.64, showing a 12% improvement over our submitted system to the DDIExtraction 2013 competition

Opinion Mining and Sentiment Analysis of Online Drug Reviews as a Pharmacovigilance Technique

Pharmacovigilance is the science that focuses on identification and characterization of adverse effects of medications in populations when released to market. The focus of this paper is to study the prospects of exploiting drug related online reviews contributed by social media groups for finding the adverse effects of drugs using opinion mining and sentiment analysis. The experiences and opinions related to drug adverse reactions by patients or other contributors in these forums can be mined and analyzed as a facilitator for pharmacovigilance. This review paper highlights the usability of opinion mining and sentiment analysis as one of the approaches for pharmacovigilance. DOI: 10.17762/ijritcc2321-8169.150711

Participatory Militias: An Analysis of an Armed Movement's Online Audience

Armed groups of civilians known as "self-defense forces" have ousted the powerful Knights Templar drug cartel from several towns in Michoacan. This militia uprising has unfolded on social media, particularly in the "VXM" ("Valor por Michoacan," Spanish for "Courage for Michoacan") Facebook page, gathering more than 170,000 fans. Previous work on the Drug War has documented the use of social media for real-time reports of violent clashes. However, VXM goes one step further by taking on a pro-militia propagandist role, engaging in two-way communication with its audience. This paper presents a descriptive analysis of VXM and its audience. We examined nine months of posts, from VXM's inception until May 2014, totaling 6,000 posts by VXM administrators and more than 108,000 comments from its audience. We describe the main conversation themes, post frequency and relationships with offline events and public figures. We also characterize the behavior of VXM's most active audience members. Our work illustrates VXM's online mobilization strategies, and how its audience takes part in defining the narrative of this armed conflict. We conclude by discussing possible applications of our findings for the design of future communication technologies.Comment: Participatory Militias: An Analysis of an Armed Movement's Online Audience. Saiph Savage, Andres Monroy-Hernandez. CSCW: ACM Conference on Computer-Supported Cooperative Work 201

Classifying Relations using Recurrent Neural Network with Ontological-Concept Embedding

Relation extraction and classification represents a fundamental and challenging aspect of Natural Language Processing (NLP) research which depends on other tasks such as entity detection and word sense disambiguation. Traditional relation extraction methods based on pattern-matching using regular expressions grammars and lexico-syntactic pattern rules suffer from several drawbacks including the labor involved in handcrafting and maintaining large number of rules that are difficult to reuse. Current research has focused on using Neural Networks to help improve the accuracy of relation extraction tasks using a specific type of Recurrent Neural Network (RNN). A promising approach for relation classification uses an RNN that incorporates an ontology-based concept embedding layer in addition to word embeddings. This dissertation presents several improvements to this approach by addressing its main limitations. First, several different types of semantic relationships between concepts are incorporated into the model; prior work has only considered is-a hierarchical relationships. Secondly, a significantly larger vocabulary of concepts is used. Thirdly, an improved method for concept matching was devised. The results of adding these improvements to two state-of-the-art baseline models demonstrated an improvement to accuracy when evaluated on benchmark data used in prior studies