Automatic assessment of adverse drug reaction reports with interactive visual exploration

作者：Zongyang Gao, Yu Yang, Ruogu Meng, Jinyang Yu, Liang Zhou · 发表于：Scientific Reports · 年份：2022 · DOI：10.1038/s41598-022-10887-5 · 被引用次数：6 · 研究领域：Data Visualization and Analytics、Data-Driven Disease Surveillance、Health Policy Implementation Science

A large number of adverse drug reaction (ADR) reports are collected yearly through the spontaneous report system (SRS). However, experienced experts from ADR monitoring centers (ADR experts, hereafter) reviewed only a few reports based on current policies. Moreover, the causality assessment of ADR reports was conducted according to the official approach based on the WHO-UMC system, a knowledge- and labor-intensive task that highly relies on an individual's expertise. Our objective is to devise a method to automatically assess ADR reports and support the efficient exploration of ADRs interactively. Our method could improve the capability to assess and explore a large volume of ADR reports and aid reporters in self-improvement. We proposed a workflow for assisting the assessment of ADR reports by combining an automatic assessment prediction model and a human-centered interactive visualization method. Our automatic causality assessment model (ACA model)-an ordinal logistic regression model-automatically assesses ADR reports under the current causality category. Based on the results of the ACA model, we designed a warning signal to indicate the degree of the anomaly of ADR reports. An interactive visualization technique was used for exploring and examining reports extended by automatic assessment of the ACA model and the warning signal. We applied our method to the SRS report dataset of the year 2019, collected in Guangdong province, China. Our method is evaluated by comparing au...