Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. OBJECTIVE: The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration-approved immune checkpoint inhibitors. METHODS: In our framework, we first used the Food and Drug Administration's Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. RESULTS: By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. CONCLUSIONS: We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".