D rug W atch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information (2024)

Abstract: Drug safety research is crucial for maintaining public health, often requiring comprehensive data support. However, the resources currently available to the public are limited and fail to provide a comprehensive understanding of the relationship between drugs and their side effects. This paper introduces “DrugWatch”, an easy-to-use and interactive multi-source information visualisation platform for drug safety study. It allows users to understand common side effects of drugs and their statistical information, flexibly retrieve relevant medical reports, or annotate their own medical texts with our automated annotation tool. Supported by NLP technology and enriched with interactive visual components, we are committed to providing researchers and practitioners with a one-stop information analysis, retrieval, and annotation service. The demonstration video is available at https://www.youtube.com/watch?v=RTqDgxzETjw. We also deployed an online demonstration system at https://drugwatch.net/.

## introduction the use of medications is a cornerstone of modern disease management, yet their potential for adverse reactions can pose safety risks. adverse drug reactions (adrs) have been reported to be the most common cause of hospitalisation and rank as the fourth or sixth leading cause of death @xcite . in addition to the inherent risks of medications themselves, certain drugs may exhibit unpredictable sensitivities in specific patient @xcite . furthermore, there is also a risk of interactions when multiple medications are used concurrently. therefore, to ensure public health safety, professionals such as physicians, drug developers, and regulatory officials often need to comprehensively understand, * equal contribution. assess, and monitor medication safety information from various sources. to benefit the monitoring of adverse drug events, the world health organization (who) and numerous countries or regions have established databases for spontaneous case reporting, such as vigibase (uppsala monitoring centre, 2024), fda adverse event reporting system (faers; u.s. food and drug administration 2024), and eudravigilance (european medicines agency, 2024). these databases typically offer interactive query tools to assist users in visualising statistical data from the reporting system. for example, the faers dashboard enable users to search for specific drug products or reaction terms, and offers visual charts illustrating the distribution of corresponding reports by year, demographic details, reaction categories, etc. these databases serve as crucial sources of information for drug safety research. however, the presence of reports in spontaneous reporting systems does not imply a causal relationship between the drug and the reported adverse reactions. the context in which adverse reactions occur is often complex and may be related to the underlying disease, concurrent medication use, or other factors. therefore, relying solely on statistical information from spontaneous reporting systems is insufficient for a deeper understanding of drug-induced adverse reactions. researchers often need access to more detailed information for analysis, much of which is embedded within textual descriptions. in this paper, we introduce drugwatch, a multisource data visualisation platform that integrates information from structured, textual and user-held data on drug safety. it comprises two primary subplatforms: drugwatch search and drugwatch annotate. drugwatch search offers users visualised statistical data sourced from faers and pubmed, along with robust support for fine-grained pubmed medical case report retrieval. drugwatch annotate empowers users to annotate their private data and visualise the resulting annotations. for drugwatch search, similar to the faers dashboard, we enable users to search for drugs or adverse reactions and visualise the statistical information provided by the faers database. however, we additionally utilise event extraction techniques to retrieve textual context and present statistics extracted from text data for user queries. we gather medical case reports related to adrs from pubmed and extract structured information about adverse events using the approach proposed by @xcite . we present the statistical information of the extracted results in a similar way to that of the faers data for easy comparison. additionally, we provide users with a list of pubmed literature and abstracts associated with their search queries, enabling them to conveniently access detailed descriptions of the events in medical texts. users can also customise more granular search criteria, such as limiting results based on patient age or gender, to filter the search results. for drugwatch annotate, we integrate several pre-trained models such as flan-t5 @xcite and uie @xcite , and an llm, i.e., mistral-7b @xcite , that enables users to perform fine-grained pharmacovigilance event extraction on their private data. these models support the extraction of subject, treatment, and effect information for adverse drug events (ades) and potential therapeutic events (ptes), along with their sub-arguments (e.g., demographic information and drug administration details). we support the visualisation of annotation results, allowing users to quickly try a single data point through a demo window or batch-view the visualised annotations for each data entry. we have pre-processed and visualised manual annotations from the phee dataset @xcite and different model predictions for direct model comparison and selection. the contributions of this paper can be summarised as follows: drugwatch entrance frontend drugwatch search drugwatch annotate react sveltekit flexible search drug information statistical information display pubmed case report retrieval live annotation & visualisation bulk annotation & visualisation: model comparison, result filtering backend openfda api data collection ade classification ade extraction normalisation & linking drugwatch search drugwatch annotate flan-t5 model uie model mistral-7b model flask faers pubmed phee user data drugwatch consists of two sub-platforms: drug-watch search and drugwatch annotate. the overall architecture is illustrated in figure 1 . drugwatch search is designed for flexible drug and ade search. it presents not only fundamental information about drugs but also statistical information from the faers database and pubmed literature collections. additionally, it enables users to access relevant ade case reports seamlessly. its front-end is implemented using react (meta platforms, inc., 2024), creating a smooth and interactive user experience. on the server side, we utilise the flask @xcite framework to manage api requests and handle data processing from local databases. drugwatch annotate provides automated prediction and visualisation services for user-held data. users can instantly or in bulk extract ades from their data using our built-in fine-tuned models or llms. they can easily visualise the event arguments extracted for each data instance, compare prediction results from different models, and conveniently filter results. we preload the phee dataset for direct comparison purposes. the front-end of drugwatch annotate is built with sveltekit (rich harris, 2024), ensuring fast responsiveness and a clear, visually appealing user experience. the backend continues to utilise flask. flexible search users can search for individual drugs or side effects and their respective combinations. a combination search for drugs and side effects is currently not available from the search entrance, but users can filter search results by side effects (or drugs). furthermore, for any queried drug or side effect, the platform provides the option to refine search results based on specific demographic filters, including patient gender, age group (or exact age), and nationality. for a visual guide, see figure a1 and figure a2 in appendix. ## model evaluation ade classification evaluation the classifier is trained and evaluated on the ade dataset @xcite . it contains approximately 4k ade-related sentences and 16k negative sentences. we sample an equal number of negative sentences as the positive ones for training and evaluation. ## conclusion and future work drugwatch is a multi-source data visualisation and annotation platform for drug safety research. it aims to provide a comprehensive, interactive information retrieval experience. we are committed to alleviating the inconvenience researchers often face when navigating multiple platforms to access information on drug adverse reactions. to achieve this, we integrate statistical and textual information from the spontaneous case report systems (i.e., faers) and medical literature databases (i.e., pubmed), allowing users to conduct interactive searches. we also support users to annotate and visualise their own text, laying the foundation for subsequent indepth private data analysis. in future work, we will consider further expanding data sources and supporting more granular searches. in particular, we currently do not support statistical analysis of users' private data due to data security considerations, which is an issue we are actively working to address. furthermore, we are considering integrating literature summaries or question-answer components into the system to support summarisation or questioning of retrieved pubmed texts, enabling users to learn diverse information seamlessly.