Clinical Data Cleaner

该 Skill 使用 Python 清洗和规范化临床试验数据，针对 DM、LB、VS 域执行 SDTM 必填字段校验、缺失值处理、异常值检测与日期标准化，并生成清洗后的 CSV 和 JSON 审计报告。它提供类接口与命令行调用，但不包含完整 CDISC 全领域校验或实际监管提交。

renhaosu2024

热度 71.45

最近更新 2026-05-11 17:31:20

分类：效率依赖：代码执行 / 读写文件应用场景：数据处理 / 工作流 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add clinical-data-cleaner

Metadata

name: clinical-data-cleaner
description: Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails.
allowed-tools: Read Write Bash Edit
license: MIT
metadata: {"skill-author":"AIPOCH","version":"2.0"}

Clinical Data Cleaner

Clean, validate, and standardize clinical trial data to meet CDISC SDTM standards for regulatory submissions to FDA or EMA.

Quick Start

from scripts.main import ClinicalDataCleaner

# Initialize for Demographics domain
cleaner = ClinicalDataCleaner(domain='DM')

# Clean data with default settings
cleaned = cleaner.clean(raw_data)

# Save with audit trail
cleaner.save_report('output.csv')

Core Capabilities

1. SDTM Domain Validation

cleaner = ClinicalDataCleaner(domain='DM')  # or 'LB', 'VS'
is_valid, missing = cleaner.validate_domain(data)

Required Fields:

DM: STUDYID, USUBJID, SUBJID, RFSTDTC, RFENDTC, SITEID, AGE, SEX, RACE
LB: STUDYID, USUBJID, LBTESTCD, LBCAT, LBORRES, LBORRESU, LBSTRESC, LBDTC
VS: STUDYID, USUBJID, VSTESTCD, VSORRES, VSORRESU, VSSTRESC, VSDTC

2. Missing Value Handling

cleaner = ClinicalDataCleaner(
    domain='DM',
    missing_strategy='median'  # mean, median, mode, forward, drop
)
cleaned = cleaner.handle_missing_values(data)

3. Outlier Detection

cleaner = ClinicalDataCleaner(
    domain='LB',
    outlier_method='domain',  # iqr, zscore, domain
    outlier_action='flag'     # flag, remove, cap
)
flagged = cleaner.detect_outliers(data)

Clinical Thresholds:

Parameter	Range	Unit
Glucose	50-500	mg/dL
Hemoglobin	5-20	g/dL
Systolic BP	70-220	mmHg

4. Date Standardization

standardized = cleaner.standardize_dates(data)
# Converts to ISO 8601: 2023-01-15T09:30:00

5. Complete Pipeline

cleaner = ClinicalDataCleaner(
    domain='DM',
    missing_strategy='median',
    outlier_method='iqr',
    outlier_action='flag'
)
cleaned_data = cleaner.clean(data)
cleaner.save_report('output.csv')

Output Files:

output.csv - Cleaned SDTM data
output.report.json - Audit trail for regulatory submission

CLI Usage

# Clean demographics
python scripts/main.py \
  --input dm_raw.csv \
  --domain DM \
  --output dm_clean.csv \
  --missing-strategy median \
  --outlier-method iqr \
  --outlier-action flag

# Clean lab data with clinical thresholds
python scripts/main.py \
  --input lb_raw.csv \
  --domain LB \
  --output lb_clean.csv \
  --outlier-method domain

Common Patterns

See references/common-patterns.md for detailed examples:

Regulatory Submission Preparation
Interim Analysis Data Preparation
Database Migration Cleanup
External Lab Data Integration

Troubleshooting

See references/troubleshooting.md for solutions to:

Validation failures
Date parsing errors
Memory errors with large datasets
Outlier detection issues

Quality Checklist

Pre-Cleaning:

IACUC approval obtained (animal studies)
Sample size adequately powered
Randomization method documented

Post-Cleaning:

Validate against CDISC SDTM IG
Review all cleaning actions in audit trail
Test import to analysis software

References

references/sdtm_ig_guide.md - CDISC SDTM Implementation Guide
references/domain_specs.json - Domain-specific field requirements
references/outlier_thresholds.json - Clinical outlier thresholds
references/common-patterns.md - Detailed usage patterns
references/troubleshooting.md - Problem-solving guide

Skill ID: 189 | Version: 2.0 | License: MIT