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`

python scripts/main.py --patient patient_data.json --drug drug_profile.json --doses "[50, 100, 150]"

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

from scripts.main import DigitalTwinBuilder

builder = DigitalTwinBuilder()
twin = builder.build_twin(patient_data)
results = twin.simulate_drug_regimen(drug_profile, dose_range)

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

{
  "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_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"]
}

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

# Python dependencies
pip install -r requirements.txt

Digital Twin Patient Builder | Skills Pool

Output Type	Description
`efficacy_prediction`	Efficacy prediction results
`toxicity_prediction`	Toxicity reaction prediction
`optimal_dose`	Optimal dose recommendation

Digital Twin Patient Builder

Digital Twin Patient Builder

Digital Twin Patient Builder (ID: 208)

Function Overview

Use Cases

Input

Output

Usage

Command Line Usage

Parameters

Python API

Technical Architecture

Dependencies

Example Data Format

Patient Data (patient_data.json)

Drug Profile (drug_profile.json)

Model Principles

References

Risk Assessment

Security Checklist

Prerequisites

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

Healthcare Cdss Patterns

Drug Discovery

Qmd

Attack Tree Construction

Azure Ai Anomalydetector Java

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