Multi-Aspect Learning for Drug Information Extraction

Abstract

In modern healthcare, medical professionals play a vital role in promoting health. One fundamental task of clinical physicians and pharmacists is to work together to provide treatment plans for patients. Therefore, they need to understand comprehensive drug information. Despite the importance of accurate and comprehensive drug information, several challenges complicate the process of accessing and utilizing this information: (1) data variety. Drug information is dispersed across various sources, including scientific articles, clinical texts, and social media texts. Each type of source has its own format and characteristics, making it difficult to consolidate and interpret the data uniformly. (2) complexity of terminology. The presence of complex terminologies, abbreviations, and jargon in drug information adds another layer of difficulty. (3) dynamic nature. Drug information continuously evolves with new research findings, updates to drug labels, and emerging side effects. These challenges make it difficult for medical professionals to efficiently gather and utilize the necessary information from different sources, potentially impacting the quality of patient care.

To address these challenges, it is crucial to develop methods that can automatically extract and integrate drug information from various aspects. In this context, an aspect can refer to any perspective of data or type of feature. This includes, but is not limited to, sentence-level perspective, corpus-level perspective, syntactic feature, semantic feature, domain-specific feature, etc. Multi-aspect learning (MAL) is a methodological framework that aims to integrate and align multiple perspectives of data or multiple types of features to effectively extract drug-related information. By leveraging MAL, the model can more comprehensively understand the texts and ensure that the extracted information is both accurate and relevant.