Leveraging deep neural networks and semantic similarity measures for medical concept normalisation in user reviews

© 2018 Rossiiskii Gosudarstvennyi Gumanitarnyi Universitet.All Rights Reserved. Nowadays a new yet powerful tool for drug repurposing and hypothesis generation emerged. Text mining of different domains like scientific libraries or social media has proven to be reliable in that application. One particular task in that area is medical concept normalization, i.e. mapping a disease mention to a concept in a controlled vocabulary, like Unified Medical Language System (UMLS). This task is challenging due to the differences in language of health care professionals and social media users. To bridge this gap, we developed end-to-end architectures based on bidirectional Long Short-Term Memory and Gated Recurrent Units. In addition, we combined an attention mechanism with our model. We have done an exploratory study on hyperparameters of proposed architectures and compared them with the effective baseline for classification based on convolutional neural networks. A qualitative examination of the mentions in user reviews dataset collected from popular online health information platforms as well as quantitative one both show improvements in the semantic representation of health-related expressions in user reviews about drugs

Leveraging deep neural networks and semantic similarity measures for medical concept normalisation in user reviews

© 2018 Rossiiskii Gosudarstvennyi Gumanitarnyi Universitet Society of Cosmetic Chemists. All rights reserved. Nowadays a new yet powerful tool for drug repurposing and hypothesis generation emerged. Text mining of different domains like scientific libraries or social media has proven to be reliable in that application. One particular task in that area is medical concept normalization, i.e. mapping a disease mention to a concept in a controlled vocabulary, like Unified Medical Language System (UMLS). This task is challenging due to the differences in language of health care professionals and social media users. To bridge this gap, we developed end-to-end architectures based on bidirectional Long Short-Term Memory and Gated Recurrent Units. In addition, we combined an attention mechanism with our model. We have done an exploratory study on hyperparameters of proposed architectures and compared them with the effective baseline for classification based on convolutional neural networks. A qualitative examination of the mentions in user reviews dataset collected from popular online health information platforms as well as quantitative one both show improvements in the semantic representation of health-related expressions in user reviews about drugs

The Ethical Implications of Collecting Data from Online Health Communities

Online communities can provide researchers with a raft of knowledge about a range of populations and groups. While ethical considerations in online research are complicated and nuanced, they are often underreported in existing research. Issues concerning the use of verbatim quotes from online communities, seeking consent, and protecting from harm and distinctions between private and public spaces in particular have generated much debate in recent years. We advocate for greater thought to be given to the ethical implications of online research and reflect on what should be considered public information, the protection of anonymity, and how to protect online users from harm. This case study provides an insight into the practicalities of conducting online research using examples from research conducted by four doctoral students