跳到正文

药品不良事件信号检测

从真实不良事件报告中计算与解释信号指标,区分报告模式、统计关联和已确认的因果证据。

开源精选医疗业务9 个文件1,738 Stars浏览次数 0mims-harvard/ToolUniverse Apache-2.0版本 8c6ed47
开始使用

下载前阅读 SKILL.md 中的使用条件,并确认所用工具支持对应能力。

输入资料
药物、事件与查询条件 真实报告资料和分析环境 阅读本节完整说明
交付内容
不良事件信号分析 计算过程与证据说明 阅读本节完整说明
  1. 获取完整包,保留配套文件的目录结构。
  2. 按照原文准备所需工具、依赖与输入资料。
  3. 阅读对应使用步骤与输入输出说明,在自己的环境中验证结果。

使用提示摘自原文,配套文件保留来源版本;实际运行条件以各包说明为准。

正文

获取原文件

REPORT_TEMPLATE.md

Report File Structure

File: [DRUG]_adverse_event_report.md

# Adverse Drug Event Signal Detection Report: [DRUG]

**Generated**: [Date] | **Drug**: [Generic Name] | **ChEMBL ID**: [ID]
**Safety Signal Score**: [XX/100] ([INTERPRETATION])

---

## Executive Summary

[2-3 paragraph summary of key findings]

**Key Safety Signals**:
1. [Strongest signal with PRR/ROR]
2. [Second signal]
3. [Third signal]

**Regulatory Status**: [Boxed warning Y/N] | [Withdrawn Y/N] | [Restrictions]

---

## 1. Drug Identification
[Phase 0 output]

## 2. FAERS Adverse Event Profile
[Phase 1 output]

## 3. Disproportionality Analysis
[Phase 2 output]

## 4. FDA Label Safety Information
[Phase 3 output]

## 5. Mechanism-Based Context
[Phase 4 output]

## 6. Comparative Safety Analysis
[Phase 5 output]

## 7. Drug-Drug Interactions & PGx Risk
[Phase 6 output]

## 8. Literature Evidence
[Phase 7 output]

## 9. Risk Assessment
[Phase 8 output]

## 10. Clinical Recommendations

### 10.1 Monitoring Recommendations
| Parameter | Frequency | Rationale |
|-----------|-----------|-----------|
| [Lab test] | [Frequency] | [Why] |

### 10.2 Risk Mitigation Strategies
| Risk | Mitigation | Evidence |
|------|-----------|----------|
| [Risk] | [Strategy] | [Source] |

### 10.3 Patient Counseling Points
- [Point 1]
- [Point 2]

### 10.4 Populations at Higher Risk
| Population | Risk Factor | Recommendation |
|-----------|-------------|----------------|
| [Group] | [Factor] | [Action] |

---

## 11. Completeness Checklist
[See below]

## 12. Data Sources
[All tools and databases used with timestamps]

Completeness Checklist

Phase 0: Drug Disambiguation

  • Generic name resolved
  • ChEMBL ID obtained
  • DrugBank ID obtained
  • Drug class identified
  • Mechanism of action stated
  • Primary target identified
  • Blackbox/withdrawal status checked

Phase 1: FAERS Profiling

  • Top adverse events queried (>=15 events)
  • Seriousness distribution obtained
  • Outcome distribution obtained
  • Age distribution obtained
  • Death-related events counted
  • Reporter country distribution obtained

Phase 2: Disproportionality Analysis

  • PRR calculated for >= 10 adverse events
  • ROR with 95% CI for each event
  • IC with 95% CI for each event
  • Signal strength classified for each
  • Demographics stratified for strong signals

Phase 3: FDA Label

  • Boxed warnings checked (or confirmed none)
  • Contraindications extracted
  • Warnings and precautions extracted
  • Adverse reactions from label
  • Drug interactions from label
  • Special populations (pregnancy, geriatric, pediatric)

Phase 4: Mechanism Context

  • Target safety profile (OpenTargets)
  • OpenTargets adverse events queried
  • ADMET predictions (if SMILES available)

Phase 5: Comparative Analysis

  • At least 1 class comparison performed
  • Class-wide vs drug-specific signals identified
  • Aggregate class AEs computed (if applicable)

Phase 6: DDIs & PGx

  • DDIs from FDA label extracted
  • PharmGKB queried
  • Dosing guidelines checked
  • FDA PGx biomarkers checked

Phase 7: Literature

  • PubMed searched (>=10 articles)
  • OpenAlex citation analysis (if time permits)
  • Key safety publications cited

Phase 8: Risk Assessment

  • Safety Signal Score calculated (0-100)
  • Each signal evidence-graded (T1-T4)
  • Score interpretation provided

Phase 9: Report

  • Report file created and saved
  • Executive summary written
  • Monitoring recommendations provided
  • Risk mitigation strategies listed
  • Patient counseling points included
  • All sources cited

CONTACT HAONAN

联系haonan

医疗 AI · 产品与独立开发

刘浩南

关注医疗 AI 从 demo 到真实落地,聚焦产品化、商业化与院内工作流效率提升。

12年医疗产研经验,大湾区头部医疗AI公司前产品总监,独立开发者

医疗 AI 产品化医疗 AI 商业化院内工作流效率
刘浩南的微信联系二维码
微信扫一扫,联系 haonan