Name: Tooluniverse Adverse Event Detection
Author: Zaoqu-Liu

Tooluniverse Adverse Event Detection

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.

Zaoqu-Liu44 星标2026年3月7日

职业
分类: 计算化学

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:

Signal quantification first - Every adverse event must have PRR/ROR/IC with confidence intervals
Serious events priority - Deaths, hospitalizations, life-threatening events always analyzed first
Multi-source triangulation - FAERS + FDA labels + OpenTargets + DrugBank + literature
Context-aware assessment - Distinguish drug-specific vs class-wide vs confounding signals
Report-first approach - Create report file FIRST, update progressively
Evidence grading mandatory - T1 (regulatory/boxed warning) through T4 (computational)
English-first queries - Always use English drug names in tool calls, respond in user's language

Adverse Drug Event Signal Detection & Analysis

KEY PRINCIPLES:

Signal quantification first - Every adverse event must have PRR/ROR/IC with confidence intervals
Serious events priority - Deaths, hospitalizations, life-threatening events always analyzed first
Multi-source triangulation - FAERS + FDA labels + OpenTargets + DrugBank + literature
Context-aware assessment - Distinguish drug-specific vs class-wide vs confounding signals
Report-first approach - Create report file FIRST, update progressively
Evidence grading mandatory - T1 (regulatory/boxed warning) through T4 (computational)
English-first queries - Always use English drug names in tool calls, respond in user's language

Strength	PRR	ROR Lower CI	IC Lower CI	Clinical Action
Strong	>= 5.0	>= 3.0	>= 2.0	Immediate investigation required
Moderate	3.0-4.9	2.0-2.9	1.0-1.9	Active monitoring recommended
Weak	2.0-2.9	1.0-1.9	0-0.9	Routine monitoring, watch for trends
No signal	< 2.0	< 1.0	< 0	Standard pharmacovigilance

Tooluniverse Adverse Event Detection

Adverse Drug Event Signal Detection & Analysis

Tooluniverse Adverse Event Detection

Adverse Drug Event Signal Detection & Analysis

When to Use

Workflow Overview

Phase 0: Input Parsing & Drug Disambiguation

0.1 Resolve Drug Identity

0.2 Output for Report

Phase 1: FAERS Adverse Event Profiling

1.1 Query FAERS for Adverse Events

1.2 Get Serious Events Breakdown

1.3 MedDRA Hierarchy Rollup

1.4 Output for Report

Phase 2: Disproportionality Analysis (Signal Detection)

2.1 Calculate Signal Metrics

2.2 Signal Detection Criteria

2.3 Signal Strength Classification

2.4 Demographic Stratification of Key Signals

2.5 Output for Report

Phase 3: FDA Label Safety Information

3.1 Extract Label Sections

3.2 Handling No Results

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