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Prompting for Few-shot Adverse Drug Reaction Recognition from Online Reviews
Conference proceeding

Prompting for Few-shot Adverse Drug Reaction Recognition from Online Reviews

Chia-Hsuan Chang, Fang-Yu Chang, San-Yih Hwang and Christopher C. Yang
2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), pp 168-175
26 Jun 2023
DOI: https://doi.org/10.1109/ICHI57859.2023.00032

Abstract

adverse drug reaction Drugs few-shot learning Medical services Migraine named-entity recognition online review prompt learning Safety Social networking (online) Surveillance User-generated content
The growing popularity of social network sites (SNS) contributes to the abundance of healthcare-related user-generated content (UGC), which can be used for identifying adverse drug reactions (ADR). Most previous works on automatic ADR detection, however, require a large amount of labeled data to reach acceptable recognition performance, deemed labor-intensive and inefficient when building an ADR detection model for UGC. To address this issue, this study proposes to apply LightNER [1], a few-shot Named Entity Recognition (NER) approach that exploits the power of a pre-trained language model (i.e., BART) and introduces a small set of prompt parameters to enable few-shot learning. Our approach first warms up the prompt parameters for learning knowledge for NER from rich-resource datasets. Then, the approach only requires a few labeled UGC to fine-tune the parameters for satisfactory ADR recognition performance. In our experiment, we collect a review dataset for a migraine drug called MigraineReviews and adopt three rich-resource datasets, including one general-purpose dataset and two datasets for clinical notes. The experimental results demonstrate that BART with prompt parameters helps transfer knowledge across datasets even if they are of different domains, thus reducing the need for a large amount of labeled ADR data. Our approach performs competitively with a limited size of labeled data (e.g., 5-shots).

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