SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting
Submission Type: Regular Long Paper
Submission Track: Resources and Evaluation
Submission Track 2: NLP Applications
Keywords: Symptom Recognition
TL;DR: Introducing SYMPTOMIFY: a comprehensive resource of patient reports for symptom recognition, enriched with annotation explanations and background knowledge about symptoms.
Abstract: 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 evaluate performance across various methods and learning paradigms, paving the way for future comparisons and benchmarking.
Submission Number: 4502
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