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

Opinion Mining and Sentiment Analysis of Online Drug Reviews as a Pharmacovigilance Technique

Pharmacovigilance is the science that focuses on identification and characterization of adverse effects of medications in populations when released to market. The focus of this paper is to study the prospects of exploiting drug related online reviews contributed by social media groups for finding the adverse effects of drugs using opinion mining and sentiment analysis. The experiences and opinions related to drug adverse reactions by patients or other contributors in these forums can be mined and analyzed as a facilitator for pharmacovigilance. This review paper highlights the usability of opinion mining and sentiment analysis as one of the approaches for pharmacovigilance. DOI: 10.17762/ijritcc2321-8169.150711

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

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

Effectiveness of a structured stimulated spontaneous safety monitoring of medicines reporting program in strengthening pharmacovigilance system in Tanzania

The study was funded partly by EDCTP under SMERT project and TMDA during SSSSM program implementation and data collection, and ASCEND project during data analysis.Under-reporting of adverse drug events (ADEs) is a challenge facing developing countries including Tanzania. Given the high magnitude of under-reporting, it was necessary to develop and assess the effectiveness of a ‘structured stimulated spontaneous safety monitoring’ (SSSSM) reporting program of ADEs which aimed at strengthening pharmacovigilance system in Tanzania. A quasi-experimental design and data mining technique were used to assess the effect of intervention after the introduction of program in seven tertiary hospitals. ADEs reports were collected from a single group and compared for 18 months before (July 2017 to December, 2018) and after the program (January 2019 to June 2020). Out of 16,557 ADEs reports, 98.6% (16,332) were reported after intervention and 0.1% (23) death related to adverse drug reactions (ADRs) were reported. Reports increased from 20 to 11,637 after intervention in Dar es salaam, 49 to 316 in Kilimanjaro and 17 to 77 in Mbeya. The population-based reporting ratio per 1,000,000 inhabitants increased from 2 reports per million inhabitants in 2018 to 85 reports in 2019. The SSSSM program can increase the reporting rate of ADEs and was useful in detecting signals from all types of medicines. This was first effective developed spontaneous program to monitor medicine safety in Tanzania.Publisher PDFPeer reviewe