digital-twin-patient-builder
# Digital Twin Patient Builder (ID: 208)
## Function Overview
Build a "digital twin" model of a patient, integrating genotype, clinical history, and imaging data to test the efficacy and toxicity of different drug doses in a virtual environment.
## Use Cases
- Personalized drug treatment plan design
- Drug dose optimization
- Adverse reaction risk assessment
- Clinical trial virtual simulation
## Input
| Data Type | Description | Format |
|---------|------|------|
| `genotype` | Patient genotype data (SNPs, CNVs) | JSON |
| `clinical_history` | Clinical history and laboratory indicators | JSON |
| `imaging_features` | Imaging features (MRI, CT, etc.) | JSON |
## Output
| Output Type | Description |
|---------|------|
| `efficacy_prediction` | Efficacy prediction results |
| `toxicity_prediction` | Toxicity reaction prediction |
| `optimal_dose` | Optimal dose recommendation |
## Usage
### Command Line Usage
```bash
python scripts/main.py --patient patient_data.json --drug drug_profile.json --doses "[50, 100, 150]"
```
## Parameters
| Parameter | Type | Default | Required | Description |
|-----------|------|---------|----------|-------------|
| `--patient` | string | - | Yes | Path to patient data JSON file |
| `--drug` | string | - | Yes | Path to drug profile JSON file |
| `--doses` | string | - | Yes | Dose range to test (JSON array format) |
| `--output`, `-o` | string | - | No | Output file path for simulation results |
| `--simulation-days` | int | 30 | No | Number of days to simulate |
| `--timestep` | float | 0.5 | No | Simulation timestep in days |
### Python API
```python
from scripts.main import DigitalTwinBuilder
builder = DigitalTwinBuilder()
twin = builder.build_twin(patient_data)
results = twin.simulate_drug_regimen(drug_profile, dose_range)
```
## Technical Architecture
```
digital-twin-patient-builder/
├── SKILL.md # This file
├── scripts/
│ └── main.py # Core implementation
│
├── Core Components:
│ ├── PatientProfile # Patient profile management
│ ├── GenotypeModel # Genotype modeling
│ ├── ClinicalModel # Clinical data modeling
│ ├── ImagingModel # Imaging feature modeling
│ ├── DigitalTwin # Digital twin main class
│ ├── PharmacokineticModel # Pharmacokinetic model
│ └── DrugSimulator # Drug simulator
```
## Dependencies
- numpy >= 1.21.0
- scipy >= 1.7.0
- pandas >= 1.3.0
## Example Data Format
### Patient Data (patient_data.json)
```json
{
"patient_id": "P001",
"genotype": {
"CYP2D6": "*1/*4",
"TPMT": "*1/*3C",
"SNPs": {"rs12345": "AG", "rs67890": "CC"}
},
"clinical": {
"age": 58,
"weight": 70.5,
"height": 170,
"lab_values": {"creatinine": 1.2, "alt": 45, "ast": 38},
"comorbidities": ["hypertension", "diabetes"]
},
"imaging": {
"tumor_volume": 45.2,
"perfusion_rate": 0.85,
"texture_features": {"entropy": 5.2, "uniformity": 0.45}
}
}
```
### Drug Profile (drug_profile.json)
```json
{
"drug_name": "ExampleDrug",
"drug_class": "chemotherapy",
"metabolizing_enzymes": ["CYP2D6", "CYP3A4"],
"target_genes": ["EGFR", "KRAS"],
"pk_params": {
"clearance": 15.5,
"volume_distribution": 45.0,
"half_life": 8.0
},
"efficacy_biomarkers": ["tumor_reduction", "survival_rate"],
"toxicity_markers": ["neutropenia", "hepatotoxicity"]
}
```
## Model Principles
1. **Genotype Modeling**: Parse drug metabolizing enzyme genotypes to predict metabolic phenotypes (ultrarapid/normal/poor metabolizer)
2. **Physiological Modeling**: Calculate personalized pharmacokinetic parameters based on age, weight, and organ function
3. **Imaging Modeling**: Extract tumor features to predict drug responsiveness
4. **Integrated Model**: Multi-modal data fusion to build a comprehensive digital twin
5. **Drug Simulation**: PBPK (physiologically-based pharmacokinetics) + PD (pharmacodynamics) model
## References
- PBPK modeling guidelines (FDA, 2018)
- Pharmacogenomics in precision medicine (Nature Reviews, 2020)
## Risk Assessment
| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python scripts with tools | High |
| Network Access | External API calls | High |
| File System Access | Read/write data | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Data handled securely | Medium |
## Security Checklist
- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] API requests use HTTPS only
- [ ] Input validated against allowed patterns
- [ ] API timeout and retry mechanisms implemented
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no internal paths exposed)
- [ ] Dependencies audited
- [ ] No exposure of internal service architecture
## Prerequisites
```bash
# Python dependencies
pip install -r requirements.txt
```
## Evaluation Criteria
### Success Metrics
- [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable
### Test Cases
1. **Basic Functionality**: Standard input → Expected output
2. **Edge Case**: Invalid input → Graceful error handling
3. **Performance**: Large dataset → Acceptable processing time
## Lifecycle Status
- **Current Stage**: Draft
- **Next Review Date**: 2026-03-06
- **Known Issues**: None
- **Planned Improvements**:
- Performance optimization
- Additional feature support
标签
skill
ai
