Exploring convolutional neural networks and topic models for user profiling from drug reviews

© 2017 Springer Science+Business Media, LLC Pharmacovigilance, and generally applications of natural language processing models to healthcare, have attracted growing attention over the recent years. In particular, drug reactions can be extracted from user reviews posted on the Web, and automated processing of this information 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 either 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 extensions of topic models and convolutional neural networks (CNN). We apply single-task and multi-task learning approaches to this problem. Based on a real-world dataset mined from a health-related web site, we conclude that while CNNs perform best in terms of predicting demographic information by jointly learning different user attributes, topic models provide additional information and reflect gender-specific and age-specific symptom profiles that may be of interest for a researcher

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

Exploring convolutional neural networks and topic models for user profiling from drug reviews

© 2017 Springer Science+Business Media, LLC Pharmacovigilance, and generally applications of natural language processing models to healthcare, have attracted growing attention over the recent years. In particular, drug reactions can be extracted from user reviews posted on the Web, and automated processing of this information 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 either 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 extensions of topic models and convolutional neural networks (CNN). We apply single-task and multi-task learning approaches to this problem. Based on a real-world dataset mined from a health-related web site, we conclude that while CNNs perform best in terms of predicting demographic information by jointly learning different user attributes, topic models provide additional information and reflect gender-specific and age-specific symptom profiles that may be of interest for a researcher

Exploring convolutional neural networks and topic models for user profiling from drug reviews

© 2017 Springer Science+Business Media, LLC Pharmacovigilance, and generally applications of natural language processing models to healthcare, have attracted growing attention over the recent years. In particular, drug reactions can be extracted from user reviews posted on the Web, and automated processing of this information 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 either 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 extensions of topic models and convolutional neural networks (CNN). We apply single-task and multi-task learning approaches to this problem. Based on a real-world dataset mined from a health-related web site, we conclude that while CNNs perform best in terms of predicting demographic information by jointly learning different user attributes, topic models provide additional information and reflect gender-specific and age-specific symptom profiles that may be of interest for a researcher

Exploring convolutional neural networks and topic models for user profiling from drug reviews

© 2017 Springer Science+Business Media, LLC Pharmacovigilance, and generally applications of natural language processing models to healthcare, have attracted growing attention over the recent years. In particular, drug reactions can be extracted from user reviews posted on the Web, and automated processing of this information 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 either 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 extensions of topic models and convolutional neural networks (CNN). We apply single-task and multi-task learning approaches to this problem. Based on a real-world dataset mined from a health-related web site, we conclude that while CNNs perform best in terms of predicting demographic information by jointly learning different user attributes, topic models provide additional information and reflect gender-specific and age-specific symptom profiles that may be of interest for a researcher