Demographic prediction based on user reviews about medications

Drug reactions can be extracted from user reviews provided on the Web, and processing this information in an automated way represents a novel and exciting approach to personalized medicine and wide-scale drug tests. In medical applications, demographic information regarding the authors of these reviews such as age and gender is of primary importance; however, existing studies usually assume that this information is available or overlook the issue entirely. In this work, we propose and compare several approaches to automated mining of demographic information from user-generated texts. We compare modern natural language processing techniques, including feature rich classifiers, extensions of topic models, and deep neural networks (both convolutional and recurrent architectures) for this problem

Review of trends in health social media analysis

This paper surveys recent publications (2008-2017) on using social media data to study public health. The survey describes the main topics being discussed in forums and presents short information about methods and tools used for analysis health social media. We put especial attention on adverse drug reaction detection problem (ADR)

Classification of drugs reviews using W-LRSVM model

Opinion mining provided less opportunity to discuss their experiences about drugs so reviewing about it was difficult. Recent findings show that online reviews and blogs on drugs are important for patients, marketers and industries. Collecting the information for drugs from the website and analyzing is a challenge. A model is designed by proposing an algorithm which crawls information from the web to analyze reviews of drugs. Reviews were crawled for five different drugs using the algorithm. The W-Bayesian Logistic Regression and Support Vector Machine (W-LRSVM) model was trained for different split ratios to obtain the accuracy of 97.46%. Experimental results on reviews of five different drugs showed that the proposed model gave better results compared to other classifier

Automated detection of adverse drug reactions from social media posts with machine learning

© Springer International Publishing AG 2018. Adverse drug reactions can have serious consequences for patients. Social media is a source of information useful for detecting previously unknown side effects from a drug since users publish valuable information about various aspects of their lives, including health care. Therefore, detection of adverse drug reactions from social media becomes one of the actual tools for pharmacovigilance. In this paper, we focus on identification of adverse drug reactions from user reviews and formulate this problem as a binary classification task. We developed a machine learning classifier with a set of features for resolving this problem. Our feature-rich classifier achieves significant improvements on a benchmark dataset over baseline approaches and convolutional neural networks

End-to-end deep framework for disease named entity recognition using social media data

© 2017 IEEE. A growing interest in the natural language processing methods applied to healthcare applications has been observed in the recent years. In particular, new drug pharmacological properties can be derived patient observations shared in social media forums. Developing approaches designed to automatically retrieve this information is of no low interest for personalized medicine and wide-scale drug tests. The full potential of the effective exploitation of both textual data and published biological data for drug research often goes untapped mostly because of the lack of tools and focused methodologies to curate and integrate the data and transform it into new, experimentally testable hypotheses. Deep learning architectures have shown promising results for a wide range of tasks. In this work, we propose to address a challenging problem by applying modern deep neural networks for disease named entity recognition. An essential step for this task is recognition of disease mentions and medical concept nor-malization, which is highly difficult with simple string matching approaches. We cast the task as an end-to-end problem, solved using two architectures based on recurrent neural networks and pre-trained word embeddings. We show that it is possible to assess the practicability of using social media data to extract representative medical concepts for pharmacovigilance or drug repurposing

A machine learning approach to classification of drug reviews in Russian

© 2017 IEEE. The automatic extraction of drug side effects from social media has gained popularity in pharmacovigilance. Information extraction methods tailored to medical subjects are essential for the task of drug repurposing and finding drug reactions. In this article, we focus on extracting information about side effects and symptoms in users' reviews about medications in Russian. We manually develop a real-world dataset by crawling user reviews from a health-related website and annotate a set of reviews on a sentence level. The paper addresses the classification problem with more than two classes, comparing a simple bag-of-words baseline and a feature-rich machine learning approach

Social media mining for identification and exploration of health-related information from pregnant women

Widespread use of social media has led to the generation of substantial amounts of information about individuals, including health-related information. Social media provides the opportunity to study health-related information about selected population groups who may be of interest for a particular study. In this paper, we explore the possibility of utilizing social media to perform targeted data collection and analysis from a particular population group -- pregnant women. We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information. To identify potentially pregnant women, we employ simple rule-based searches that attempt to detect pregnancy announcements with moderate precision. To further filter out false positives and noise, we employ a supervised classifier using a small number of hand-annotated data. We then collect their posts over time to create longitudinal health timelines and attempt to divide the timelines into different pregnancy trimesters. Finally, we assess the usefulness of the timelines by performing a preliminary analysis to estimate drug intake patterns of our cohort at different trimesters. Our rule-based cohort identification technique collected 53,820 users over thirty months from Twitter. Our pregnancy announcement classification technique achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user timelines. Analysis of the timelines revealed that pertinent health-related information, such as drug-intake and adverse reactions can be mined from the data. Our approach to using user timelines in this fashion has produced very encouraging results and can be employed for other important tasks where cohorts, for which health-related information may not be available from other sources, are required to be followed over time to derive population-based estimates.Comment: 9 page