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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.

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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 events

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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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