When Curiosity Signals Danger: Predicting Health Crises Through Online Medication Inquiries
This work addresses patient safety by enabling early intervention through automated detection of high-risk online queries, though it is incremental as it builds on existing NLP methods with a new dataset.
The study tackled the problem of detecting critical medication-related questions from online forums that may signal health crises, by introducing a novel annotated dataset and benchmarking six traditional classifiers and three LLM-based approaches, showing their potential for real-time triage systems.
Online medical forums are a rich and underutilized source of insight into patient concerns, especially regarding medication use. Some of the many questions users pose may signal confusion, misuse, or even the early warning signs of a developing health crisis. Detecting these critical questions that may precede severe adverse events or life-threatening complications is vital for timely intervention and improving patient safety. This study introduces a novel annotated dataset of medication-related questions extracted from online forums. Each entry is manually labelled for criticality based on clinical risk factors. We benchmark the performance of six traditional machine learning classifiers using TF-IDF textual representations, alongside three state-of-the-art large language model (LLM)-based classification approaches that leverage deep contextual understanding. Our results highlight the potential of classical and modern methods to support real-time triage and alert systems in digital health spaces. The curated dataset is made publicly available to encourage further research at the intersection of patient-generated data, natural language processing, and early warning systems for critical health events. The dataset and benchmark are available at: https://github.com/Dvora-coder/LLM-Medication-QA-Risk-Classifier-MediGuard.