Aims Many websites provide a means for individuals to share their experiences and knowledge about different drugs. Such User-Generated Content (UGC) can be a rich data source to study emerging drug use practices and trends. This study examined UGC on extra-medical use of loperamide among illicit opioid users. Methods A website that allows for the free discussion of illicit drugs and is accessible for public viewing was selected for analysis. Web-forum posts were retrieved using web crawlers and retained in a local text database. The database was queried to extract posts with a mention of loperamide and relevant brand/slang terms. Over 1,290 posts were identified. A random sample of 258 posts was coded using NVivo to identify intent, dosage, and side-effects of loperamide use. Results There has been an increase in discussions related to loperamide’s use by non-medical opioid users, especially in 2010–2011. Loperamide was primarily discussed as a remedy to alleviate a broad range of opioid withdrawal symptoms, and was sometimes referred to as “poor man’s” methadone. Typical doses ranged 70–100 mg per day, much higher than an indicated daily dose of 16 mg. Conclusions This study suggests that loperamide is being used extra-medically to self-treat opioid withdrawal symptoms. There is a growing demand among people who are opioid dependent for drugs to control withdrawal symptoms, and loperamide appears to fit that role. The study also highlights the potential of the Web as a “leading edge” data source in identifying emerging drug use practices.

Over the years, Twitter has become one of the largest communication platforms providing key data to various applications such as brand monitoring, trend detection, among others. Entity linking is one of the major tasks in natural language understanding from tweets and it associates entity mentions in text to corresponding entries in knowledge bases in order to provide unambiguous interpretation and additional con- text. State-of-the-art techniques have focused on linking explicitly mentioned entities in tweets with reasonable success. However, we argue that in addition to explicit mentions i.e. The movie Gravity was more ex- pensive than the mars orbiter mission entities (movie Gravity) can also be mentioned implicitly i.e. This new space movie is crazy. you must watch it!. This paper introduces the problem of implicit entity linking in tweets. We propose an approach that models the entities by exploiting their factual and contextual knowledge. We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1) the importance of linking implicit entities and its value addition to the standard entity linking task, and 2) the importance of exploiting contextual knowledge associated with an entity for linking their implicit mentions. We also make the ground truth dataset publicly available to foster the research in this new research area.Comment: This paper was accepted at the Extended Semantic Web Conference 2016 as a full research track pape

Introduction Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is estimated that more than 90% of AEs go unreported. In this landscape, embracing assistive technologies at scale becomes necessary to obtain a higher yield of AEs, to maintain compliance, and transform the PV professional work life. Aim The aim of this study was to identify areas across the PV value chain that can be augmented by cognitive service solutions using the methodologies of contextual analysis and cognitive load theory. It will also provide a framework of how to validate these PV cognitive services leveraging the acceptable quality limit approach. Methods The data used to train the cognitive service were an annotated corpus consisting of 20,000 ICSRS from which we developed a framework to identify and validate 40 cognitive services ranging from information extraction to complex decision making. This framework addresses the following shortcomings: (1) needing subject-matter expertise (SME) to match the artificial intelligence (AI) model predictions to the gold standard, commonly referred to as 'ground truth' in the AI space, (2) ground truth inconsistencies, (3) automated validation of prediction missing context, and (4) auto-labeling causing inaccurate test accuracy. The method consists of (1) conducting contextual analysis, (2) assessing human cognitive workload, (3) determining decision points for applying artificial intelligence (AI), (4) defining the scope of the data, or annotated corpus required for training and validation of the cognitive services, (5) identifying and standardizing PV knowledge elements, (6) developing cognitive services, and (7) reviewing and validating cognitive services. Results By applying the framework, we (1) identified 51 decision points as candidates for AI use, (2) standardized the process to make PV knowledge explicit, (3) embedded SMEs in the process to preserve PV knowledge and context, (4) standardized acceptability by using established quality inspection principles, and (5) validated a total of 126 cognitive services. Conclusion The value of using AI methodologies in PV is compelling; however, as PV is highly regulated, acceptability will require assurances of quality, consistency, and standardization. We are proposing a foundational framework that the industry can use to identify and validate services to better support the gathering of quality data and to better serve the PV professional.

IntroductionThere is increasing interest in social digital media (SDM) as a data source for pharmacovigilance activities; however, SDM is considered a low information content data source for safety data. Given that pharmacovigilance itself operates in a high-noise, lower-validity environment without objective ‘gold standards’ beyond process definitions, the introduction of large volumes of SDM into the pharmacovigilance workflow has the potential to exacerbate issues with limited manual resources to perform adverse event identification and processing. Recent advances in medical informatics have resulted in methods for developing programs which can assist human experts in the detection of valid individual case safety reports (ICSRs) within SDM.ObjectiveIn this study, we developed rule-based and machine learning (ML) models for classifying ICSRs from SDM and compared their performance with that of human pharmacovigilance experts.MethodsWe used a random sampling from a collection of 311,189 SDM posts that mentioned Roche products and brands in combination with common medical and scientific terms sourced from Twitter, Tumblr, Facebook, and a spectrum of news media blogs to develop and evaluate three iterations of an automated ICSR classifier. The ICSR classifier models consisted of sub-components to annotate the relevant ICSR elements and a component to make the final decision on the validity of the ICSR. Agreement with human pharmacovigilance experts was chosen as the preferred performance metric and was evaluated by calculating the Gwet AC1 statistic (gKappa). The best performing model was tested against the Roche global pharmacovigilance expert using a blind dataset and put through a time test of the full 311,189-post dataset.ResultsDuring this effort, the initial strict rule-based approach to ICSR classification resulted in a model with an accuracy of 65% and a gKappa of 46%. Adding an ML-based adverse event annotator improved the accuracy to 74% and gKappa to 60%. This was further improved by the addition of an additional ML ICSR detector. On a blind test set of 2500 posts, the final model demonstrated a gKappa of 78% and an accuracy of 83%. In the time test, it took the final model 48 h to complete a task that would have taken an estimated 44,000 h for human experts to perform.ConclusionThe results of this study indicate that an effective and scalable solution to the challenge of ICSR detection in SDM includes a workflow using an automated ML classifier to identify likely ICSRs for further human SME review.Electronic supplementary materialThe online version of this article (10.1007/s40264-018-0641-7) contains supplementary material, which is available to authorized users.

IntroductionRegulations are increasing the scope of activities that fall under the remit of drug safety. Currently, individual case safety report (ICSR) collection and collation is done manually, requiring pharmacovigilance professionals to perform many transactional activities before data are available for assessment and aggregated analyses. For a biopharmaceutical company to meet its responsibilities to patients and regulatory bodies regarding the safe use and distribution of its products, improved business processes must be implemented to drive the industry forward in the best interest of patients globally. Augmented intelligent capabilities have already demonstrated success in capturing adverse events from diverse data sources. It has potential to provide a scalable solution for handling the ever-increasing ICSR volumes experienced within the industry by supporting pharmacovigilance professionals’ decision-making.ObjectiveThe aim of this study was to train and evaluate a consortium of cognitive services to identify key characteristics of spontaneous ICSRs satisfying an acceptable level of accuracy determined by considering business requirements and effective use in a real-world setting. The results of this study will serve as supporting evidence for or against implementing augmented intelligence in case processing to increase operational efficiency and data quality consistency.MethodsA consortium of ten cognitive services to augment aspects of ICSR processing were identified and trained through deep-learning approaches. The input data for model training were 20,000 ICSRs received by Celgene drug safety over a 2-year period. The data were manually made machine-readable through the process of transcription, which converts images into text. The machine-readable documents were manually annotated for pharmacovigilance data elements to facilitate the training and testing of the cognitive services. Once trained by cognitive developers, the cognitive services’ output was reviewed by pharmacovigilance subject-matter experts against the accepted ground-truth for correctness and completeness. To be considered adequately trained and functional, each cognitive service was required to reach a threshold of F1 or accuracy score ≥ 75%.ResultsAll ten cognitive services under development have reached an evaluative score ≥ 75% for spontaneous ICSRs.ConclusionAll cognitive services under development have achieved the minimum evaluative threshold to be considered adequately trained, demonstrating how machine-learning and natural language processing techniques together provide accurate outputs that may augment pharmacovigilance professionals’ processing of spontaneous ICSRs quickly and accurately. The intention of augmented intelligence is not to replace the pharmacovigilance professional, but rather support them in their consistent decision-making so that they may better handle the overwhelming amount of data otherwise manually curated and monitored for ongoing drug surveillance requirements. Through this supported decision-making...

With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in a sentence. However, clinical documents often contain phrases that indicate the entities but do not contain their names. We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to create entity models, projects sentences to the entity models and identifies implicit entity mentions by evaluating semantic similarity between sentences and entity models. The evaluation with 857 sentences selected for 8 different entities shows that our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping it to improve over the baselines, and achieving F1 scores of .81 and .73 for different classes of implicit mentions. Our gold standard annotations are made available to encourage further research in the area of IER.

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

Semantic computing technologies have matured to be applicable to many critical domains such as national security, life sciences, and health care. However, the key to their success is the availability of a rich domain knowledge base. The creation and refinement of domain knowledge bases pose difficult challenges. The existing knowledge bases in the health care domain are rich in taxonomic relationships, but they lack nontaxonomic (domain) relationships. In this paper, we describe a semiautomatic technique for enriching existing domain knowledge bases with causal relationships gleaned from Electronic Medical Records (EMR) data. We determine missing causal relationships between domain concepts by validating domain knowledge against EMR data sources and leveraging semantic-based techniques to derive plausible relationships that can rectify knowledge gaps. Our evaluation demonstrates that semantic techniques can be employed to improve the efficiency of knowledge acquisition.