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tooluniverse-adverse-event-detection - Claude MCP Skill
Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, adverse event investigation, and regulatory decision support.
SEO Guide: Enhance your AI agent with the tooluniverse-adverse-event-detection tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to detect and analyze adverse drug event signals using fda faers data, drug labels, disproportionality ... Download and configure this skill to unlock new capabilities for your AI workflow.
Documentation
SKILL.md# Adverse Drug Event Signal Detection & Analysis
Automated pipeline for detecting, quantifying, and contextualizing adverse drug event signals using FAERS disproportionality analysis, FDA label mining, mechanism-based prediction, and literature evidence. Produces a quantitative Safety Signal Score (0-100) for regulatory and clinical decision-making.
**KEY PRINCIPLES**:
1. **Signal quantification first** - Every adverse event must have PRR/ROR/IC with confidence intervals
2. **Serious events priority** - Deaths, hospitalizations, life-threatening events always analyzed first
3. **Multi-source triangulation** - FAERS + FDA labels + OpenTargets + DrugBank + literature
4. **Context-aware assessment** - Distinguish drug-specific vs class-wide vs confounding signals
5. **Report-first approach** - Create report file FIRST, update progressively
6. **Evidence grading mandatory** - T1 (regulatory/boxed warning) through T4 (computational)
7. **English-first queries** - Always use English drug names in tool calls, respond in user's language
**Reference files** (in this directory):
- `PHASE_DETAILS.md` - Detailed tool calls, code examples, and output templates per phase
- `REPORT_TEMPLATE.md` - Full report template and completeness checklist
- `TOOL_REFERENCE.md` - Tool parameter reference and fallback chains
- `QUICK_START.md` - Quick examples and common drug names
---
## When to Use
Apply when user asks:
- "What are the safety signals for [drug]?"
- "Detect adverse events for [drug]"
- "Is [drug] associated with [adverse event]?"
- "What are the FAERS signals for [drug]?"
- "Compare safety of [drug A] vs [drug B] for [adverse event]"
- "What are the serious adverse events for [drug]?"
- "Are there emerging safety signals for [drug]?"
- "Post-market surveillance report for [drug]"
- "Pharmacovigilance signal detection for [drug]"
**Differentiation from tooluniverse-pharmacovigilance**: This skill focuses specifically on **signal detection and quantification** using disproportionality analysis (PRR, ROR, IC) with statistical rigor, produces a quantitative **Safety Signal Score (0-100)**, and performs **comparative safety analysis** across drug classes.
---
## Workflow Overview
```
Phase 0: Input Parsing & Drug Disambiguation
Parse drug name, resolve to ChEMBL ID, DrugBank ID
Identify drug class, mechanism, and approved indications
|
Phase 1: FAERS Adverse Event Profiling
Top adverse events by frequency
Seriousness and outcome distributions
Demographics (age, sex, country)
|
Phase 2: Disproportionality Analysis (Signal Detection)
Calculate PRR, ROR, IC with 95% CI for each AE
Apply signal detection criteria
Classify signal strength (Strong/Moderate/Weak/None)
|
Phase 3: FDA Label Safety Information
Boxed warnings, contraindications
Warnings and precautions, adverse reactions
Drug interactions, special populations
|
Phase 4: Mechanism-Based Adverse Event Context
Target-based AE prediction (OpenTargets safety)
Off-target effects, ADMET predictions
Drug class effects comparison
|
Phase 5: Comparative Safety Analysis
Compare to drugs in same class
Identify unique vs class-wide signals
Head-to-head disproportionality comparison
|
Phase 6: Drug-Drug Interactions & Risk Factors
Known DDIs causing AEs
Pharmacogenomic risk factors (PharmGKB)
FDA PGx biomarkers
|
Phase 7: Literature Evidence
PubMed safety studies, case reports
OpenAlex citation analysis
Preprint emerging signals (EuropePMC)
|
Phase 8: Risk Assessment & Safety Signal Score
Calculate Safety Signal Score (0-100)
Evidence grading (T1-T4) for each signal
Clinical significance assessment
|
Phase 9: Report Synthesis & Recommendations
Monitoring recommendations
Risk mitigation strategies
Completeness checklist
```
---
## Phase Summaries
### Phase 0: Input Parsing & Drug Disambiguation
Resolve drug name to ChEMBL ID, DrugBank ID. Get mechanism of action, blackbox warning status, targets, and approved indications.
- **Tools**: `OpenTargets_get_drug_chembId_by_generic_name`, `OpenTargets_get_drug_mechanisms_of_action_by_chemblId`, `OpenTargets_get_drug_blackbox_status_by_chembl_ID`, `drugbank_get_safety_by_drug_name_or_drugbank_id`, `drugbank_get_targets_by_drug_name_or_drugbank_id`, `OpenTargets_get_drug_indications_by_chemblId`
### Phase 1: FAERS Adverse Event Profiling
Query FAERS for top adverse events, seriousness distribution, outcomes, demographics, and death-related events. Filter serious events by type (death, hospitalization, life-threatening). Get MedDRA hierarchy rollup.
- **Tools**: `FAERS_count_reactions_by_drug_event`, `FAERS_count_seriousness_by_drug_event`, `FAERS_count_outcomes_by_drug_event`, `FAERS_count_patient_age_distribution`, `FAERS_count_death_related_by_drug`, `FAERS_count_reportercountry_by_drug_event`, `FAERS_filter_serious_events`, `FAERS_rollup_meddra_hierarchy`
### Phase 2: Disproportionality Analysis (Signal Detection)
**CRITICAL PHASE**. For each top adverse event (at least 15-20), calculate PRR, ROR, IC with 95% CI. Classify signal strength. Stratify strong signals by demographics.
- **Tools**: `FAERS_calculate_disproportionality`, `FAERS_stratify_by_demographics`
- **Signal criteria**: PRR >= 2.0 AND lower CI > 1.0 AND N >= 3
- **Strength**: Strong (PRR >= 5), Moderate (PRR 3-5), Weak (PRR 2-3)
- See `PHASE_DETAILS.md` for full signal classification table
### Phase 3: FDA Label Safety Information
Extract boxed warnings, contraindications, warnings/precautions, adverse reactions, drug interactions, and special population info. Note: `{error: {code: "NOT_FOUND"}}` is normal when a section does not exist.
- **Tools**: `FDA_get_boxed_warning_info_by_drug_name`, `FDA_get_contraindications_by_drug_name`, `FDA_get_warnings_by_drug_name`, `FDA_get_adverse_reactions_by_drug_name`, `FDA_get_drug_interactions_by_drug_name`, `FDA_get_pregnancy_or_breastfeeding_info_by_drug_name`, `FDA_get_geriatric_use_info_by_drug_name`, `FDA_get_pediatric_use_info_by_drug_name`, `FDA_get_pharmacogenomics_info_by_drug_name`
### Phase 4: Mechanism-Based Adverse Event Context
Get target safety profile, OpenTargets adverse events, ADMET toxicity predictions (if SMILES available), and drug warnings.
- **Tools**: `OpenTargets_get_target_safety_profile_by_ensemblID`, `OpenTargets_get_drug_adverse_events_by_chemblId`, `ADMETAI_predict_toxicity`, `ADMETAI_predict_CYP_interactions`, `OpenTargets_get_drug_warnings_by_chemblId`
### Phase 5: Comparative Safety Analysis
Head-to-head comparison with class members using `FAERS_compare_drugs`. Aggregate class AEs. Identify class-wide vs drug-specific signals.
- **Tools**: `FAERS_compare_drugs`, `FAERS_count_additive_adverse_reactions`, `FAERS_count_additive_seriousness_classification`
### Phase 6: Drug-Drug Interactions & Risk Factors
Extract DDIs from FDA label, DrugBank, and DailyMed. Query PharmGKB for pharmacogenomic risk factors and dosing guidelines. Check FDA PGx biomarkers.
- **Tools**: `FDA_get_drug_interactions_by_drug_name`, `drugbank_get_drug_interactions_by_drug_name_or_id`, `DailyMed_parse_drug_interactions`, `PharmGKB_search_drugs`, `PharmGKB_get_drug_details`, `PharmGKB_get_dosing_guidelines`, `fda_pharmacogenomic_biomarkers`
### Phase 7: Literature Evidence
Search PubMed, OpenAlex, and EuropePMC for safety studies, case reports, and preprints.
- **Tools**: `PubMed_search_articles`, `openalex_search_works`, `EuropePMC_search_articles`
### Phase 8: Risk Assessment & Safety Signal Score
Calculate Safety Signal Score (0-100) from four components: FAERS signal strength (0-35), serious AEs (0-30), FDA label warnings (0-25), literature evidence (0-10). Grade each signal T1-T4. See `PHASE_DETAILS.md` for scoring rubric.
### Phase 9: Report Synthesis
Generate comprehensive markdown report with executive summary, all phase outputs, monitoring recommendations, risk mitigation strategies, patient counseling points, and completeness checklist. See `REPORT_TEMPLATE.md` for full template.
---
## Common Analysis Patterns
| Pattern | Description | Phases |
|---------|-------------|--------|
| **Full Safety Profile** | Comprehensive report for regulatory/safety reviews | All (0-9) |
| **Specific AE Investigation** | "Does [drug] cause [event]?" | 0, 2, 3, 7 |
| **Drug Class Comparison** | Compare 3-5 drugs for specific AE | 0, 2, 5 |
| **Emerging Signal Detection** | Screen for signals not in FDA label | 1, 2, 3, 7 |
| **PGx Risk Assessment** | Genetic risk factors for AEs | 0, 6 |
| **Pre-Approval Assessment** | New drugs with limited FAERS data | 4, 7 |
---
## Edge Cases
- **No FAERS reports**: Skip Phases 1-2; rely on FDA label, mechanism predictions, literature
- **Generic vs Brand name**: Try both in FAERS; use `OpenTargets_get_drug_chembId_by_generic_name` to resolve
- **Drug combinations**: Use `FAERS_count_additive_adverse_reactions` for aggregate class analysis
- **Confounding by indication**: Compare AE profile to the disease being treated; note limitation in report
- **Drugs with boxed warnings**: Score component automatically 25/25 for label warnings; prioritize boxed warning eventsSignals
Information
- Repository
- mims-harvard/ToolUniverse
- Author
- mims-harvard
- Last Sync
- 3/12/2026
- Repo Updated
- 3/12/2026
- Created
- 2/19/2026
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