Drug Reaction Discriminator within Encoder-Decoder Neural Network Model: COVID-19 Pandemic Case Study

Abstract

Social networks become widely used for understanding patients shared experiences, and reaching a vast audience in a matter of seconds. In particular, many health-related organizations used sentiment analysis to automatically reporting treatment issues, drug misuse, new infectious disease symptoms. Few approaches have proposed in this matter, especially for detecting different drug reaction descriptions from patients generated narratives on social networks. Most of them consisted of only detecting adverse drug reaction(ADR), but may fail to retrieve other aspect, e.g, the beneficial drug reaction or drug retroviral effects such as “relieve intraocular pressure associated with glaucoma”. In this study, we propose to develop an encoder-decoder for drug reaction discrimination that involves an enhanced distributed biomedical representation from controlled medical vocabulary such as PubMed and Clinical note MIMIC III. The embedding mechanism primarily leverages contextual information and learn from predefined clinical relationships in term of medical conditions in order to define possible drug reaction of individual meaning and multi-word expressions in the field of distributional semantics configuration that clarifies sentence's similarity in the same contextual target space, which are further share semantically common drug description meanings. Furthermore, the bidirectional sentiment inductive model are created to enhance drug reactions vectorization from real-world patients description whereby achieved higher performance in terms of disambiguating false positive and/or negative assessments. As a result, we achieved an 85.2% accuracy performance and the architecture shows a well-encoding of real-world drug entities descriptions.