Identifying adverse drug reactions from patient reviews on social media using natural language processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drugs have the potential of causing adverse reactions or side effects and prior knowledge of these reactions can help prevent hospitalizations and premature deaths. Public databases of common adverse drug reactions (ADRs) depend on individual reports from drug manufacturers and health professionals. However, this passive approach to ADR surveillance has been shown to suffer from severe under-reporting. Social media, such as online health forums where patients across the globe willingly share their drug intake experience, is a viable and rich source for detecting unreported ADRs. In this paper, we design an ADR Detection Framework (ADF) using Natural Language Processing techniques to identify ADRs in drug reviews mined from social media. We demonstrate the applicability of ADF in the domain of Diabetes by identifying ADRs associated with diabetes drugs using data extracted from three online patient-based health forums: askapatient.com, webmd.com, and iodine.com. Next, we analyze and visualize the ADRs identified and present valuable insights including prevalent and less prevalent ADRs, age and gender differences in ADRs detected, as well as the previously unknown ADRs detected by our framework. Our work could promote active (real-time) ADR surveillance and also advance pharmacovigilance research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it