Automated prediction of demographic information from medical user reviews

© 2017, Springer International Publishing AG.The advent of personalized medicine and wide-scale drug tests has led to the development of methods intended to automatically mine and extract information regarding drug reactions from user reviews. For medical purposes, it is often important to know demographic information on the authors of these reviews; however, existing studies usually either presuppose that this information is available or disregard the issue. We study automatic mining of demographic information from user-generated texts, comparing modern natural language processing techniques, including extensions of topic models and deep neural networks, for this problem on datasets mined from health-related web sites

KFU at CLEF eHealth 2017 Task 1: ICD-10 coding of English death certificates with recurrent neural networks

This paper describes the participation of the KFU team in the CLEF eHealth 2017 challenge. Specifically, we participated in Task 1, namely "Multilingual Information Extraction - ICD-10 coding" for which we implemented recurrent neural networks to automatically assign ICD-10 codes to fragments of death certificates written in English. Our system uses Long Short-Term Memory (LSTM) to map the input sequence into a vector representation, and then another LSTM to decode the target sequence from the vector. We initialize the input representations with word embeddings trained on user posts in social media. The encoderdecoder model obtained F-measure of 85.01% on a full test set with significant improvement as compared to the average score of 62.2% for all participants' approaches. We also obtained significant improvement from 26.1% to 44.33% on an external test set as compared to the average score of the submitted runs

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

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

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

© 2017 Elena Tutubalina and Sergey Nikolenko. Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction

Automated prediction of demographic information from medical user reviews

© 2017, Springer International Publishing AG.The advent of personalized medicine and wide-scale drug tests has led to the development of methods intended to automatically mine and extract information regarding drug reactions from user reviews. For medical purposes, it is often important to know demographic information on the authors of these reviews; however, existing studies usually either presuppose that this information is available or disregard the issue. We study automatic mining of demographic information from user-generated texts, comparing modern natural language processing techniques, including extensions of topic models and deep neural networks, for this problem on datasets mined from health-related web sites

Automated prediction of demographic information from medical user reviews

© 2017, Springer International Publishing AG.The advent of personalized medicine and wide-scale drug tests has led to the development of methods intended to automatically mine and extract information regarding drug reactions from user reviews. For medical purposes, it is often important to know demographic information on the authors of these reviews; however, existing studies usually either presuppose that this information is available or disregard the issue. We study automatic mining of demographic information from user-generated texts, comparing modern natural language processing techniques, including extensions of topic models and deep neural networks, for this problem on datasets mined from health-related web sites