---name: coagulation-thrombosis-agent description: AI-powered analysis of coagulation disorders, thrombosis risk prediction, anticoagulation management, and platelet function assessment using machine learning. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• coagulation-thrombosis-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

Coagulation and Thrombosis Agent

The Coagulation and Thrombosis Agent provides AI-driven analysis of hemostatic disorders, thrombosis risk assessment, and anticoagulation management. It integrates coagulation cascade modeling, platelet function analysis, and machine learning for personalized thrombosis prevention.

When to Use This Skill

• When assessing venous thromboembolism (VTE) risk in hospitalized patients.
• For anticoagulation dose optimization (warfarin, DOACs).
• To analyze coagulation panel results and identify bleeding/clotting disorders.
• For platelet morphology and function assessment.
• When managing thrombosis in myeloproliferative neoplasms (MPNs).

Core Capabilities

1. VTE Risk Prediction: Machine learning models predict deep vein thrombosis (DVT) and pulmonary embolism (PE) risk using clinical and laboratory features.

2. Anticoagulation Optimization: AI-guided dosing for warfarin (incorporating pharmacogenomics) and monitoring for DOACs.

3. Coagulation Cascade Analysis: Interprets PT, aPTT, fibrinogen, D-dimer, and specialized assays to diagnose coagulopathies.

4. Platelet Analysis: CNN-based morphology analysis predicting bleeding and thrombosis risk from peripheral smear images.

5. DIC Scoring: Automated disseminated intravascular coagulation (DIC) scoring and monitoring.

6. MPN Thrombosis Risk: Specialized models for thrombosis prediction in polycythemia vera, essential thrombocythemia.

Workflow

1. Input: Coagulation lab results, patient demographics, clinical risk factors, platelet images (optional).

2. Risk Assessment: Apply ML models for VTE, bleeding, or DIC risk scores.

3. Dosing Optimization: Generate anticoagulation recommendations.

4. Monitoring: Track INR/anti-Xa trends and alert on deviations.

5. Diagnosis: Pattern recognition for coagulation disorders.

6. Output: Risk scores, dosing recommendations, diagnostic suggestions, monitoring alerts.

Example Usage

User: "Calculate VTE risk for this hospitalized patient and optimize LMWH prophylaxis."

Agent Action:

python3 Skills/Hematology/Coagulation_Thrombosis_Agent/thrombosis_analyzer.py \
    --patient_data patient_demographics.json \
    --labs coagulation_panel.csv \
    --risk_model improved_padua \
    --anticoagulant lmwh \
    --renal_function egfr_45 \
    --output vte_assessment.json

Risk Models Implemented

Model	Application	Key Features
Padua (Enhanced)	Medical VTE risk	11 clinical factors + ML enhancement
Caprini (AI)	Surgical VTE risk	40+ factors with ML weighting
CHADS2-VASc	Atrial fibrillation stroke risk	Standard guideline scoring
HAS-BLED	Anticoagulation bleeding risk	Major bleeding prediction
IPSET-thrombosis	MPN thrombosis	JAK2, age, prior thrombosis

Coagulation Panel Interpretation

Test	Normal Range	Elevations Suggest	Decreases Suggest
PT/INR	11-13.5s / 0.9-1.1	Warfarin, VII def, liver disease	-
aPTT	25-35s	Heparin, VIII/IX/XI def, lupus AC	-
Fibrinogen	200-400 mg/dL	Acute phase, inflammation	DIC, liver disease
D-dimer	<500 ng/mL	VTE, DIC, inflammation	-
Platelet	150-400K	Reactive, MPN	ITP, marrow failure

AI/ML Components

Deep Learning for Platelet Morphology:

• CNN analysis of peripheral smear images
• Identifies giant platelets, platelet clumps, hypogranular forms
• Predicts bleeding/thrombosis risk from morphology

VTE Prediction Models:

• Gradient boosting (XGBoost) on structured EHR data
• Incorporates labs, vitals, medications, procedures
• AUC > 0.85 for hospital-acquired VTE

Anticoagulation Dosing:

• Reinforcement learning for INR control
• Pharmacogenomic integration (CYP2C9, VKORC1)
• Real-time dose adjustment recommendations

Prerequisites

• Python 3.10+
• scikit-learn, XGBoost, PyTorch
• HL7 FHIR client (for EHR integration)
• Image analysis libraries (for platelet morphology)

Related Skills

• Flow_Cytometry_AI - For platelet function assays
• Pharmacogenomics_Agent - For anticoagulant pharmacogenomics
• Blood_Smear_Analysis - For morphology assessment

Clinical Applications

1. Hospital VTE Prevention: Real-time risk scoring in EMR
2. Anticoagulation Clinic: AI-assisted warfarin dosing
3. DIC Management: Automated scoring and transfusion guidance
4. Inherited Disorders: Pattern recognition for factor deficiencies

Author

AI Group - Biomedical AI Platform