SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting
Bosung Kim, Ndapa Nakashole · 2023
Given the high-stakes nature of healthcare decision-making, we aim to improve the efficiency of human annotators rather than replacing them with fully automated solutions.We introduce a new comprehensive resource, SYMPTOMIFY, a dataset of annotated vaccine adverse reaction reports detailing individual vaccine reactions.The dataset, consisting of over 800k reports, surpasses previous datasets in size.Notably, it features reasoning-based explanations alongside background knowledge obtained via language model knowledge harvesting.We assess data quality, and evaluate performance across various methods and learning paradigms, paving the way for future comparisons and benchmarking.1