tooluniverse-adverse-event-detection

## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

# 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

**REASONING STRATEGY — Start Here**:
Start with the signal: What adverse event was reported more than expected? (PRR >= 2.0, N >= 3, lower CI > 1.0 is the threshold). Then ask three questions in order:
1. **Biologically plausible?** Given the drug's mechanism of action and targets, does this adverse event make sense? An off-target kinase inhibitor causing cardiac events is plausible; a topical agent causing systemic toxicity needs more scrutiny. LOOK UP DON'T GUESS — use `OpenTargets_get_drug_mechanisms_of_action_by_chemblId` and `drugbank_get_targets_by_drug_name_or_drugbank_id` to check targets before asserting plausibility.
2. **Timing consistent?** Acute reactions (within hours/days) suggest immune or direct pharmacologic mechanism. Delayed reactions (weeks/months) suggest cumulative toxicity or idiosyncratic response. Check FAERS time-to-onset distribution.
3. **Could confounders explain it?** Patients taking this drug likely have the underlying disease — compare against background rate in that population, not the general population. Class-wide signals (appearing for all drugs in the class) suggest mechanism-based rather than molecule-specific toxicity.

**Causality Assessment — Naranjo Algorithm Reasoning**:
When determining whether an adverse event is drug-caused (not just associated), apply these steps systematically. LOOK UP DON'T GUESS — search FAERS and FDA labels for each criterion:
1. **Prior reports?** Are there previous conclusive reports of this reaction? Check FDA label (`FDA_get_adverse_reactions_by_drug_name`) and literature (`PubMed_search_articles`). Yes = +1.
2. **Temporal relationship?** Did the AE appear after drug administration? Onset within expected pharmacokinetic window (1-5 half-lives) = +2. Use `FAERS_stratify_by_demographics` for time-to-onset data.
3. **Dechallenge?** Did the AE improve when the drug was stopped? Positive dechallenge = +1. Look for rechallenge/dechallenge case reports in literature.
4. **Rechallenge?** Did the AE reappear when the drug was restarted? Positive rechallenge = +2 (strongest single piece of evidence for causality).
5. **Alternative causes?** Could the underlying disease, concomitant drugs, or other factors explain the AE? Check `drugbank_get_drug_interactions_by_drug_name_or_id` for interacting drugs.
6. **Dose-response?** Did the reaction worsen with higher doses or improve with lower doses? Dose-dependent AEs suggest on-target toxicity.
7. **Drug level confirmation?** Was the drug detected in body fluids at toxic concentrations?
- Score: Definite (>=9), Probable (5-8), Possible (1-4), Doubtful (<=0).
- Even without individual patient data, you can estimate causality from aggregate FAERS signals + label evidence + mechanistic plausibility.

**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

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## 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.

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## 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: Mech