A Large-Scale CNN Ensemble for Medication Safety Analysis

Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing drug surveillance, and data from health-related forums and medical communities can be of a great significance for estimating such effects. In this paper, we propose an end-to-end CNN-based method for predicting drug safety on user comments from healthcare discussion forums. We present an architecture that is based on a vast ensemble of CNNs with varied structural parameters, where the prediction is determined by the majority vote. To evaluate the performance of the proposed solution, we present a large-scale dataset collected from a medical website that consists of over 50 thousand reviews for more than 4000 drugs. The results demonstrate that our model significantly outperforms conventional approaches and predicts medicine safety with an accuracy of 87.17% for binary and 62.88% for multi-classification tasks

Effective Feature Representation for Clinical Text Concept Extraction

Crucial information about the practice of healthcare is recorded only in free-form text, which creates an enormous opportunity for high-impact NLP. However, annotated healthcare datasets tend to be small and expensive to obtain, which raises the question of how to make maximally efficient uses of the available data. To this end, we develop an LSTM-CRF model for combining unsupervised word representations and hand-built feature representations derived from publicly available healthcare ontologies. We show that this combined model yields superior performance on five datasets of diverse kinds of healthcare text (clinical, social, scientific, commercial). Each involves the labeling of complex, multi-word spans that pick out different healthcare concepts. We also introduce a new labeled dataset for identifying the treatment relations between drugs and diseases

Adverse drug extraction in twitter data using convolutional neural network

The study of health-related topics on social media has become a useful tool for the early detection of the different adverse medical conditions. In particular, it concerns cases related to the treatment of mental diseases, as the effects of medications here often prove to be unpredictable. In our research, we use convolutional neural networks (CNN) with word2vec embedding to classify user comments on Twitter. The aim of the classification is to reveal adverse drug reactions of users. The results obtained are highly promising, showing the overall usefulness of neural network algorithms in this kind of tasks

A Biased Topic Modeling Approach for Case Control Study from Health Related Social Media Postings

abstract: Online social networks are the hubs of social activity in cyberspace, and using them to exchange knowledge, experiences, and opinions is common. In this work, an advanced topic modeling framework is designed to analyse complex longitudinal health information from social media with minimal human annotation, and Adverse Drug Events and Reaction (ADR) information is extracted and automatically processed by using a biased topic modeling method. This framework improves and extends existing topic modelling algorithms that incorporate background knowledge. Using this approach, background knowledge such as ADR terms and other biomedical knowledge can be incorporated during the text mining process, with scores which indicate the presence of ADR being generated. A case control study has been performed on a data set of twitter timelines of women that announced their pregnancy, the goals of the study is to compare the ADR risk of medication usage from each medication category during the pregnancy. In addition, to evaluate the prediction power of this approach, another important aspect of personalized medicine was addressed: the prediction of medication usage through the identification of risk groups. During the prediction process, the health information from Twitter timeline, such as diseases, symptoms, treatments, effects, and etc., is summarized by the topic modelling processes and the summarization results is used for prediction. Dimension reduction and topic similarity measurement are integrated into this framework for timeline classification and prediction. This work could be applied to provide guidelines for FDA drug risk categories. Currently, this process is done based on laboratory results and reported cases. Finally, a multi-dimensional text data warehouse (MTD) to manage the output from the topic modelling is proposed. Some attempts have been also made to incorporate topic structure (ontology) and the MTD hierarchy. Results demonstrate that proposed methods show promise and this system represents a low-cost approach for drug safety early warning.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Named Entity Recognition using Neural Networks for Clinical Notes

International audienceCurrently, the best performance for Named Entity Recognition in medical notes is obtained by systems based on neural networks. These supervised systems require precise features in order to learn well fitted models from training data, for the purpose of recognizing medical entities like medication and Adverse Drug Events (ADE). Because it is an important issue before training the neural network, we focus our work on building comprehensive word representations (the input of the neural network), using character-based word representations and word representations. The proposed representation improves the performance of the baseline LSTM. However, it does not reach the performances of the top performing contenders in the challenge for detecting medical entities from clinical notes.Actuellement, la meilleure performance pour la reconnaissance de l'entité nommée dans les notes médicales est obtenue par des systèmes basés sur des réseaux de neurones. Ces systèmes supervisés nécessitent des caractéristiques précises afin d'apprendre des modèles bien ajustés à partir des données de formation, dans le but de reconnaître les entités médicales comme les médicaments et les événements indésirables liés aux médicaments (EIM). Parce qu'il s'agit d'une question importante avant la formation du réseau neuronal, nous concentrons notre travail sur la construction de représentations complètes de mots (l'entrée du réseau neuronal), en utilisant des représentations de mots basés sur des caractères et des représentations de mots. La représentation proposée améliore la performance de la LSTM de référence. Cependant, il n'atteint pas les performances des concurrents les plus performants dans le challenge de détection d'entités médicales à partir de notes cliniques

Adverse event detection by integrating twitter data and VAERS

Background: Vaccinehasbeenoneofthemostsuccessfulpublichealthinterventionstodate.However,vaccines are pharmaceutical products that carry risks so that many adverse events (AEs) are reported after receiving vaccines. Traditional adverse event reporting systems suffer from several crucial challenges including poor timeliness. This motivates increasing social media-based detection systems, which demonstrate successful capability to capture timely and prevalent disease information. Despite these advantages, social media-based AE detection suffers from serious challenges such as labor-intensive labeling and class imbalance of the training data. Results: Totacklebothchallengesfromtraditionalreportingsystemsandsocialmedia,weexploittheircomplementary strength and develop a combinatorial classification approach by integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after influenza vaccine. Specifically, we combine formal reports which have accurately predefined labels with social media data to reduce the cost of manual labeling; in order to combat the class imbalance problem, a max-rule based multi-instance learning method is proposed to bias positive users. Various experiments were conducted to validate our model compared with other baselines. We observed that (1) multi-instance learning methods outperformed baselines when only Twitter data were used; (2) formal reports helped improve the performance metrics of our multi-instance learning methods consistently while affecting the performance of other baselines negatively; (3) the effect of formal reports was more obvious when the training size was smaller. Case studies show that our model labeled users and tweets accurately. Conclusions: WehavedevelopedaframeworktodetectvaccineAEsbycombiningformalreportswithsocialmedia data. We demonstrate the power of formal reports on the performance improvement of AE detection when the amount of social media data was small. Various experiments and case studies show the effectiveness of our model

Text Mining Methods for Analyzing Online Health Information and Communication

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients\u27 questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit organizations and federal agencies can also diffuse preventative information in such networks for better outcomes. Researchers in health communication can mine user generated content on social networks to understand themes and derive insights into patient experiences that may be impractical to glean through traditional surveys. The main difficulty in mining social health data is in separating the signal from the noise. Social data is characterized by informal nature of content, typos, emoticons, tonal variations (e.g. sarcasm), and ambiguities arising from polysemous words, all of which make it difficult in building automated systems for deriving insights from such sources. In this dissertation, we present four efforts to mine health related insights from user generated social data. In the first effort, we build a model to identify marketing tweets on electronic cigarettes (e-cigs) and assess different topics in marketing and non-marketing messages on e-cigs on Twitter. In our next effort, we build ensemble models to classify messages on a mental health forum for triaging posts whose authors need immediate attention from trained moderators to prevent self-harm. The third effort deals with models from our participation in a shared task on identifying tweets that discuss adverse drug reactions and those that mention medication intake. In the final task, we build a classifier that identifies whether a particular tweet about the popular Juul e-cig indicates the tweeter actually using the product. Our methods range from linear classifiers (e.g., logistic regression), classical nonlinear models (e.g., nearest neighbors), recent deep neural networks (e.g., convolutional neural networks), and ensembles of all these models in using different supervised training regimens (e.g., co-training). The focus is more on task specific system building than on building specific individual models. Overall, we demonstrate that it is possible to glean insights from social data on health related topics through natural language processing and machine learning with use-cases from substance use and mental health